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Zeng, Yi, Shiqun Yin, Jiangyue Liu, and Miao Zhang. "Research of Improved FP-Growth Algorithm in Association Rules Mining." Scientific Programming 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/910281.
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Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.
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Sidhu, Shivam, Upendra Kumar Meena, Aditya Nawani, Himanshu Gupta, and Narina Thakur. "FP Growth Algorithm Implementation." International Journal of Computer Applications 93, no. 8 (2014): 6–10. http://dx.doi.org/10.5120/16233-5613.
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Lawal, Ma’aruf Mohammed, and Ogedengbe Tunde Matthew. "FP-Growth Algorithm: Mining Association Rules without Candidate Sets Generation." Kasu Journal of Computer Science 1, no. 2 (2024): 392–411. http://dx.doi.org/10.47514/kjcs/2024.1.2.0016.
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Over the years, due to modern technological advancement, unprecedented volume of data is been captured, and this has necessitated the need to mine such data to provide decision-based solution to non-trivial problems. Deploying an efficiently critical decision-based solution for handling such problems, require data mining algorithms. These evolving techniques emerged as an indispensable tools for pattern discovery in inventory data. With one notable technique being the application of Association Rule analysis, especially the Market Basket Analysis. However, mining association rules from large datasets can be daunting due to the volume of candidate sets generated by association rule algorithms like Apriori and ECLAT. Thus, candidate sets generated by these association rule based algorithms yield numerous rules, which contain both interesting and uninteresting ones. Hence, making interpretation overwhelming and decision-making challenging. On this note, this paper focused on demonstrating the efficiency of the FP-Growth algorithm in extracting relevant and interesting association rules for mining transaction itemsets over large datasets. By examining the FP-Growth algorithm design, functionality, and performance in depth analysis. The FP-Growth algorithm, which is an improved version of the Apriori algorithm is introduced with the intent to reduce the overhead costs by employing the FP-Tree data structure that efficiently encode the frequency information of itemsets in a dataset. To demonstrate performance improvement of the FP-Growth over the Apriori algorithms, the two algorithm were implemented on the WEKA data mining platform using a supermarket dataset. The performance of both algorithms is evaluated and compared in terms of computational time. The experimental results shows that the FP-Growth algorithm recorded 82.04% improvement over the Apriori algorithm. This improvement is attributed to the FP-Growth algorithm single dataset scan and the absence of candidate set generation which is inherent in the Apriori algorithm.
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Sukenda, Ari Purno Wahyu, and Sunjana. "Medicine Product Recommendation System using Apriori Algorithm and Fp-Growth Algorithm." International Journal of Psychosocial Rehabilitation 24, no. 02 (2020): 3208–11. http://dx.doi.org/10.37200/ijpr/v24i2/pr200629.
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Taktak, Wiem, and Yahya Slimani. "MS-FP-Growth: A Multi-support Version of FP-Growth Algorithm." International Journal of Hybrid Information Technology 7, no. 3 (2014): 155–66. http://dx.doi.org/10.14257/ijhit.2014.7.3.16.
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Sharma, Rahul, and Dr Manish Manoria. "Novel Approach for Frequent Pattern Algorithm for Maximizing Frequent Patterns in Effective Time." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (2012): 279–83. http://dx.doi.org/10.24297/ijct.v3i2b.2876.
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The essential aspect of mining association rules is to mine the frequent patterns. Due to native difficulty it is impossible to mine complete frequent patterns from a dense database. FP-growth algorithm has been implemented using an Array-based structure, known as the FP-tree,which is for storing compressed frequency information. Numerous experimental results have demonstrated that the algorithm performs extremely well. But in FP-growth algorithm, two traversals of FP-tree are needed for constructing the new conditional FP-tree. In this paper we present a novel Array Based Without Scanning Frequent Pattern (ABWSFP) tree technique that greatly reduces the need to traverse FP-trees, thus obtaining significantly improved performance for FP-tree based algorithms. The technique works especially well for large datasets. We then present a new algorithm which use the QFP-tree data structure in combination with the FP Tree- Experimental results show that the new algorithm outperform other algorithm in not only the speed of algorithms, but also their CPU consumption and their scalability.
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Karthik, Somu, and Velu C.M. "A Novel Prediction of Sales and Purchase Forecasting for Festival Season of Hypermarkets with Customer Dataset Using Apriori Algorithm Instead of FP-Growth Algorithm to Improve the Accuracy." ECS Transactions 107, no. 1 (2022): 12647–59. http://dx.doi.org/10.1149/10701.12647ecst.
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Aim: To predict the novel and to forecast sales for festival season hypermarkets. Materials and Methods: A total of 484 samples were collected from market datasets available in kaggle. For this two algorithms were used, one is the FP-Growth algorithm and another is Apriori algorithm. Both the algorithms were executed and compared for accuracy. Result: Apriori achieved accuracy, precision, sensitivity and specificity of 73 %,75%, 78%,and 80%, respectively, compared to 71%, 73%, 76%, 75%, and 78% by FP-Growth algorithm, 87.4%, 88.2%, 89.2%, and 93%, respectively, compared to 80.1%, 83.39%, 84%, and 86.20% by Apriori algorithm. The results were obtained with a level of significance (p<=0.310). Conclusion: The applied Apriori algorithm confirms to have higher accuracy than the FP-Growth algorithm. It was additionally found that FP-Growth calculation takes more modest time than Apriori calculation to yield novel results.
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Ardiansyah, Rizaldi, Syaiful Zuhri Harahap, and Rahma Muti Ah. "Utilizing FP-Tree and FP-Growth Algorithms for Data Mining on Medicine Sales Transactions at Khanina’s." INFORMATIKA 12, no. 3 (2024): 404–16. https://doi.org/10.36987/informatika.v12i3.5999.
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Although Khanina Pharmacy is a growing pharmacy with a lot of processes, the data processing is still done by hand. This study examines the use of the FP-Tree and FP-Growth algorithms to the medication sales transaction system. The FP-Tree and FP-Growth algorithm methods use methods or strategies to choose data in order to identify trends or intriguing details. The FP-Tree and FP-Growth algorithm approaches are two frequently used techniques in data mining. The purpose of this medicine sales transaction data is to identify concurrently purchased products. The FP-Growth Algorithm is used to find item pattern combinations. Use of FP-Tree to identify frequently occurring itemsets from a database in combination with the FP-Growth algorithm. When searching for product attachment patterns for sales tactics in decision-making rules, the Association Rule method is employed. In order to determine which medications are frequently bought by customers, we can create rules using the data in the database. The Rapidminer 5 program was used to conduct the test. This test yielded the following results: the number of itemsets created and rules constructed increased with decreasing support values.
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Raihan, Muhammad, and Sutisna. "Analisis Perbandingan Algoritma Apriori dan FP-Growth untuk Menentukan Strategi Penjualan Pada Maestro Jakarta Cafe & Space." Jurnal Indonesia : Manajemen Informatika dan Komunikasi 5, no. 3 (2024): 3147–57. http://dx.doi.org/10.35870/jimik.v5i3.994.
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There has been a decline in sales at Maestro Jakarta Cafe & Space due to a lot of competition and not optimal management of transaction data so that innovation is needed to increase sales. This study aims to compare the performance and results of the Apriori and FP-Growth algorithms in analyzing sales transaction data to determine the optimal sales strategy. This research uses the Apriori and FP-Growth methods to analyze sales transaction data by applying the Cross-Industry Standard Process for Data Mining (CRISP-DM). The data used is product sales transaction data from November 2023 to April 2024. The results of performance comparisons in processing time speed and memory usage that have been carried out show that in processing time speed the FP-Growth algorithm is slightly faster than the Apriori algorithm while in the use of memory capacity the Apriori algorithm requires a larger memory capacity than the memory capacity used by the FP-Growth algorithm. This shows that the performance of the FP-Growth algorithm is better than the Apriori algorithm. The analysis results of the Apriori and FP-Growth algorithms on sales transaction data using a minimum support value of 1% and a minimum confidence value of 100% resulted in 22 association rules. Both algorithms produce identical rules, with the only difference being the occurrence index. The results of this analysis can be used by Maestro Jakarta Cafe & Space in determining sales strategies.
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Chyad, Haitham Salman, Raniah Ali Mustafa, and Kawther Thabt Saleh. "Hand Print Recognition System based on FP-Growth Algorithm." Webology 19, no. 1 (2022): 980–1000. http://dx.doi.org/10.14704/web/v19i1/web19067.
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Hand print recognition system received great interest in the recent few years such as human-computer interaction, computer vision, and computer graphics. In this paper, proposed system for recognition human handprint based on FP-growth algorithm, the system consists of three-stage. The first stage the detection algorithm using HSV color space, canny algorithm and contrast enhancement for grayscale. In this stage separate skin area in-handprint image through first HSV color space converting RGB to HSV color space as well as conducting specific rules for determining the skin area. And then applies skin hand segmentation for the split of non skin and skin areas where hand skin color detection. After the hand detection stage, the first stage in edge detection is image smoothing through using a Gaussian filter then converted to a grayscale image after then contrast enhancement is an important step in the algorithm detection hand. Finally applying canny edge detection. The second stage extract features through apply seven moment invariants. The three-stage applying FP-growth algorithm for recognition handprint image. The system which has been proposed utilize handprint images databases, the database proposed a large data-set of human hand images, 11K Hands, that consists of palmar and dorsal sides of the human hand images dataset that collective database from 190 various subject’s handprint images is made publicly obtainable. The handprint recognition system achieved rate of 92.70%.
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Syahrir, Moch, and Lalu Zazuli Azhar Mardedi. "Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems." MATRIX : Jurnal Manajemen Teknologi dan Informatika 13, no. 2 (2023): 52–67. http://dx.doi.org/10.31940/matrix.v13i2.52-67.
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The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.
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Wang, Yushan, and Lianhong Liu. "Research on the Characteristic Model of Learners in Modern Distance Music Classroom Based on Big Data." Scientific Programming 2022 (May 14, 2022): 1–10. http://dx.doi.org/10.1155/2022/4684461.
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This paper makes in-depth research on data mining, especially association rule mining, improves the FP-tree algorithm in both the algorithm itself and the data source, and finds out a mining algorithm suitable for learner characteristics. Association rule algorithm for actor feature model mining. By establishing the characteristic model of learners in modern distance music classroom, simulation experiments are carried out on FP-tree and three improved algorithms. This paper improves the FP-tree algorithm. Firstly, we improve the algorithm itself; aiming at the problem of too many frequent itemsets, an improved key item extraction algorithm KEFP-growth based on FP-growth is proposed, which ignores the frequent itemsets that are not concerned in the analysis. Then, improvements were made in terms of data sources. In view of the problem that the data source is too large, the mining efficiency is low, and the FP-tree cannot be loaded in memory, this paper proposes a data projection algorithm, which adopts the idea of divide and conquer, divides the frequent 1-itemsets of the database into database subsets of each frequent 1-itemsets, and then mines the database subsets separately and then merges them. Finally, the KEFP-growth algorithm and the projection algorithm are combined, which can not only eliminate the mining of meaningless frequent items but also divide the data when there is a large amount of data. This paper also compares the performance of the three improved algorithms and the original FP-tree algorithm through experiments. The experiments show that the combination of the KEFP-growth algorithm and the database projection algorithm is the most suitable one for the learner feature mining of the adaptive learning system. (1) The KEFP-growth algorithm reduces the number of frequent items output by the original FP-tree algorithm by about 50%, and the mining time is reduced by 50%. (2) The data projection algorithm is more suitable for data mining with less support. When the support is 10%, the mining time of the data projection algorithm is reduced by 80% compared with the FP-tree algorithm. (3) When the support degree is 10%, the running time of the hybrid algorithm is reduced by 10% compared with the KEFP-growth algorithm and the data projection algorithm.
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Yusuf Husain, Enny Dwi Oktaviyani, and Sherly Christina. "Analisis Perbandingan Algoritma Apriori, FP-Growth, Dan Eclat dalam Menemukan Pola Pembelian Konsumen." KONSTELASI: Konvergensi Teknologi dan Sistem Informasi 3, no. 2 (2023): 231–43. http://dx.doi.org/10.24002/konstelasi.v3i2.7007.
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Abstrak. Apotek Sasameh Sehat saat ini memiliki permasalahan dalam perencanaan stok obat. Saat ini perencanaan obat masih dilakukan secara manual tanpa menggunakan sistem. Permasalahan tersebut dapat diatasi dengan menganalisis kebiasaan pembelian konsumen menggunakan associations rules mining. Penelitian ini menggunakan tiga algoritma associations rules mining yaitu Algoritma Apriori, FP-Growth dan Eclat. Terdapat perbedaan pada ketiga algoritma tersebut, yaitu dalam hal kecepatan eksekusi serta aturan yang dihasilkan. Oleh karena itu, penelitian ini akan membandingkan ketiga algoritma tersebut untuk mengetahui algoritma mana yang paling cocok untuk permasalahan Apotek Sasameh Sehat. Berdasarkan uji perbandingan algoritma, waktu eksekusi tercepat adalah Algoritma Fp growth, diikuti oleh Algoritma Eclat dan terakhir adalah Algoritma Apriori. Berdasarkan rule yang dihasilkan, ketiga algoritma pada setiap percobaan memiliki jumlah rule yang sama. Kesimpulannya adalah algoritma yang terbaik untuk menangani permasalahan di Apotek Sasameh Sehat adalah Algoritma FP-Growth. Abstract. Currently, the Sasameh Sehat Pharmacy has problems planning drug stocks. The drug planning is still done manually without using a system. These problems can be overcome by analyzing consumer buying habits using association rules mining. This study uses three association rules mining algorithms, namely the Apriori, FP-Growth and Eclat algorithms. There are differences in the three algorithms, namely in terms of execution speed and the resulting rules. Therefore, this study compared the three algorithms to find out which algorithm iwas the most suitable for the problem of the Sasameh Sehat Pharmacy. Based on the comparison test of algorithms, the fastest execution time was the Fp growth Algorithm, followed by the Eclat Algorithm and the Apriori Algorithm. Based on the rules generated, the three algorithms in each experiment had the same number of rules. Thus, it can be concluded that the best algorithm for dealing with problems at the Sasameh Sehat Pharmacy is the FP-Growth Algorithm.
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Zazuli, Lalu Zazuli Azhar Mardedi, Kartarina Kartarina, and Moch Syahrir Syahrir. "Analisis Perbandingan Algoritma Fp-Growth Dan Tpq-Apriori Dalam Menentukan Rule Based Terbaik Untuk Sistem Rekomendasi Produk." Explore 14, no. 2 (2024): 55–66. http://dx.doi.org/10.35200/ex.v14i2.112.
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The popular association rule algorithms are a priori and fp-growth, these two algorithms are very familiar among data mining researchers, however there are several weaknesses found in the association rule algorithm, including scanning the dataset for a long time in the process of searching for itemset frequencies, the use of large memory and the resulting base rules are sometimes less than optimal. In this research, the author compared the fp-growth and TPQ-apriori algorithms to analyze the base rule results of the two algorithms. TPQ-Apriori is an algorithm resulting from the development of the apriori algorithm, where the performance of the TPQ-Apriori algorithm is better than the traditional apriori algorithm in terms of the dataset scanning process in searching for itemset frequencies. For experiments, the fp-growth algorithm used the rapidminer tool while the TPQ-apriori algorithm used an application program that was built by ourselves. Meanwhile, the dataset used is sales data on CV. Charandita Kusuma NTB which has been uploaded to the Kaggle site. The base rules testing results are from the overall rule testing results with the CV sales dataset. Charandita Kusuma NTB can draw a conclusion that the larger the dataset to be processed, the more optimal the results will be if using the fp-growth rapidminer algorithm, but it is not optimal if the dataset to be processed is a small dataset. Some rules do not appear in the fp-growth algorithm with the rapidminer tool. Meanwhile, the TPQ-Apriori algorithm that has been developed is able to produce optimal rules for both large datasets and small datasets.
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LING, Xu-xiong, She-guo WANG, Yang LI, and Zai-liang MIAO. "No-header-table FP-Growth algorithm." Journal of Computer Applications 31, no. 5 (2011): 1391–94. http://dx.doi.org/10.3724/sp.j.1087.2011.01391.
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Youssef, Fakir, Khalil Salim, and Fakir Mohamed. "Extraction of association rules in a diabetic dataset using parallel FP-growth algorithm under apache spark." International Journal of Informatics and Communication Technology 13, no. 3 (2024): 445–52. https://doi.org/10.11591/ijict.v13i3.pp445-452.
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This research paper focuses on enhancing the frequent pattern growth (FP-growth) algorithm, an advanced version of the Apriori algorithm, by employing a parallelization approach using the Apache Spark framework. Association rule mining, particularly in healthcare data for predicting and diagnosing diabetes, necessitates the handling of large datasets which traditional methods may not process efficiently. Our method improves the FP-growth algorithm’s scalability and processing efficiency by leveraging the distributed computing capabilities of apache spark. We conducted a comprehensive analysis of diabetes data, focusing on extracting frequent itemsets and association rules to predict diabetes onset. The results demonstrate that our parallelized FP-growth (PFP-growth) algorithm significantly enhances prediction accuracy and processing speed, offering substantial improvements over traditional methods. These findings provide valuable insights into disease progression and management, suggesting a scalable solution for large-scale data environments in healthcare analytics.
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Mitha Rosadi and Muhammad Siddik Hasibuan. "Comparison of Apriori and FP-Growth Algorithms in Analyzing Association Rules." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 12, no. 2 (2024): 399–408. http://dx.doi.org/10.33558/piksel.v12i2.9965.
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The problem objectives of this research include the following: To implement Apriori and FP-Growth Algorithms in determining the comparison of association rules and To build a jupyter notebook application model in determining the comparison of association rules of Apriori and FP-Growth Algorithms. This research compares Apriori and FP-Growth algorithms in analyzing association rules, with a focus on implementation and model development in Jupyter Notebook. Through manual calculation using 10 transaction data samples and testing on 38,765 groceries data entries from Kaggle, differences were found in the lift results between itemsets. Apriori algorithm often shows a negative relationship between items, while FP-Growth gives a similar interpretation but with slightly different lift values, showing a different influence in the relationship between items. In addition, FP-Growth proved to be more efficient with a much faster execution time (5.2757 seconds) than Apriori (185.9585 seconds), especially in handling large datasets. The results of this study indicate that the selection of an appropriate algorithm should consider the characteristics of the dataset and the purpose of the analysis.
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Qonita Adinda Putri, Diana Yusuf, and R.Tommy Gumelar. "RANCANG BANGUN APLIKASI DATA MINING DENGAN ALGORITMA FP-GROWTH PADA DATA PENJUALAN SPAREPART MOBIL SUZUKI RADIO DALAM." Jurnal Sistem Informasi (JUSIN) 4, no. 2 (2023): 94–110. http://dx.doi.org/10.32546/jusin.v4i2.2143.
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Suzuki Radio Dalam is an automotive company operating in the automotive sector. They have been facing a challenge where sales data of spare parts has been accumulating without being effectively utilized or managed. The company has never employed data mining techniques to extract meaningful patterns or insights from this spare parts sales data. To address these issues, the researchers adopted the data mining technique known as the FP-Growth algorithm. The FP-Growth algorithm is a form of association algorithm within data mining. Association algorithms are utilized to uncover relationships and connections between variables present in a dataset. Through the application of the FP-Growth algorithm, data can be extracted through the construction of FP-Trees, which reveals insights into patterns of items purchased by customers. This method allows for the identification of frequently co-purchased items, enabling the company to devise marketing strategies aimed at boosting spare parts sales. The proposed solution involves creating a web-based platform to facilitate the FP-Growth algorithm calculations, particularly when dealing with large volumes of data. This web-based system was developed using PHP and utilizes a MySQL database. This data is then subjected to FP-Growth algorithm calculations and subsequently analyzed to generate association rules. These association rules hold valuable information about customer purchasing patterns. The implementation of this web mining solution streamlines the FP-Growth algorithm calculations, making it more manageable and efficient when dealing with substantial datasets. The resulting association rules derived from these calculations provide actionable insights for Suzuki's marketing strategies. By offering enticing promotions to customers based on the information gleaned from these association rules, the company aims to enhance spare parts sales.
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Ariestya, Winda Widya, Wahyu Supriyatin, and Ida Astuti. "MARKETING STRATEGY FOR THE DETERMINATION OF STAPLE CONSUMER PRODUCTS USING FP-GROWTH AND APRIORI ALGORITHM." Jurnal Ilmiah Ekonomi Bisnis 24, no. 3 (2019): 225–35. http://dx.doi.org/10.35760/eb.2019.v24i3.2229.
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The demand for staple products that vary among customers makes it necessary for the store to determine how the marketing strategy should be. Data mining are known as KDD (Knowledge Discovery in Database) is to digging up valuable knowledge from the data. Research purpose is to identify the right marketing strategy to sales the goods. The marketing strategy is took by analyze how much consumers demand for basic needs. The algorithms used in this research are FP (Frequent Pattern)-Growth and A-priori Algorithm. Finding combinations patterns between itemset using the Association Rule. FP-Growth algorithm is an algorithm that been used to determining a set of data in a data set that often appears on the frequency of the itemset. the KDD stages study are data cleansing, data integration, data selection, data transformation, data mining, pattern evaluation and knowledge presentation. the Testing used Rapidminer software with a minimum confidence value of 0.6 and a minimum support of 0.45. FP-Growth algorithm obtained 5 rule conclusions while Apriori Algorithm obtained 3 rule conclusions. The FP-Growth algorithm make a better decision rules than a priori algorithms in determining of marketing strategies, because it produces more decisions on how the goods sold.
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Teja Nursasongka, Raden Mas, Imam Fahrurrozi, Unan Yusmaniar Oktiawati, Umar Taufiq, Umar Farooq, and Ganjar Alfian. "Utilizing association rule mining for enhancing sales performance in web-based dashboard application." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1105. http://dx.doi.org/10.11591/ijeecs.v36.i2.pp1105-1113.
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Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales.
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Raden, Mas Teja Nursasongka Imam Fahrurrozi Unan Yusmaniar Oktiawati Umar Taufiq Umar Farooq Ganjar Alfian. "Utilizing association rule mining for enhancing sales performance in web-based dashboard application." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 2 (2024): 1105–13. https://doi.org/10.11591/ijeecs.v36.i2.pp1105-1113.
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Data is increasingly recognized as a valuable asset for generating new insights and information. Given the importance of data, businesses must always look for ways to get more value from data generated from sales transactions. In data mining, association rule mining is a good standard technique and is widely used to find interesting relationships in databases. Association rule is closely related to market basket analysis to find items that often appear together in one transaction. This study proposes the frequent pattern growth (FP-Growth) algorithm in finding association rules on sales transaction data. Our methodology includes dataset preparation for modeling, evaluation of model performance, and subsequent integration into a web-based platform. We conducted a comparative analysis of the FP-Growth algorithm against the Apriori algorithm, finding that FP-Growth outperformed Apriori in efficiency. Using the same dataset and constraint level, both algorithms produce the same number of frequent itemsets. However, in terms of computation time, FP-Growth excels by taking 2.89 seconds while Apriori takes 5.29 seconds. We integrated trained FP-Growth algorithm into a web-based dashboard application using the streamlit framework. This system is anticipated to simplify the process for businesses to identify customer purchasing patterns and improve sales.
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Djabalul Lael, Tri Ahmad, and Deskha Akmal Pramudito. "Use of Data Mining for The Analysis of Consumer Purchase Patterns with The Fpgrowth Algorithm on Motor Spare Part Sales Transactions Data." IAIC Transactions on Sustainable Digital Innovation (ITSDI) 4, no. 2 (2023): 128–36. http://dx.doi.org/10.34306/itsdi.v4i2.582.
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This study aims to analyze consumer purchasing patterns for motorcycle parts using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data. This research aims to obtain helpful information for companies in planning marketing strategies and increasing sales. The data used in this study are motorcycle parts sales transaction data from motorcycle parts stores for one year. The data is then processed using the FP-Growth algorithm to find significant purchasing patterns. The results of this study show that the FP-Growth algorithm can be used to identify substantial consumer purchasing patterns. Some purchase patterns found include a combination of often purchased products, the most active purchase time, and the most purchased product category. Using data mining and the FP-Growth algorithm can assist companies in understanding significant consumer purchasing patterns to improve the effectiveness of marketing strategies and increase sales of motorcycle parts. The novelty of this research lies in using data mining methods and FP-Growth algorithms on motorcycle parts sales transaction data to analyze consumer purchasing patterns. This research also provides valuable information for companies in planning marketing strategies and increasing sales by identifying significant consumer purchasing patterns, such as product combinations often purchased together and the most purchased product categories.
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Asyahri, Hadi Nasyuha, Jama Jalius, Abdullah Rijal, et al. "Frequent pattern growth algorithm for maximizing display items." TELKOMNIKA Telecommunication, Computing, Electronics and Control 19, no. 2 (2021): pp. 390~396. https://doi.org/10.12928/TELKOMNIKA.v19i2.16192.
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Products are goods that are available and provided in stores for sale. Products provided in stores must be arranged properly to order to attract the attention of consumers to buy. Products arranged in a store will depend on the type of store. The product arrangement at a retail store will be different from the product arrangement at a clothing store. Store display will reflect a picture that is in the store so consumers know the types of products sold by product arrangement. An attractive arrangement will stimulate the desire of consumers to buy. In data mining there are several types of methods by use including prediction, association, classification and estimation. In the prediction method there are several techniques including the frequent pattern growth (FP-growth) method. FP-growth algorithm is the development of the apriori algorithm. So, the shortcomings of the apriori algorithm are corrected by the FP-growth algorithm. FP-growth is one alternative algorithm that can be used to determine the set of data that most often appears (frequent itemset) in a data set. Results of research on the application of the FP-growth algorithm to maximizing the display of goods. It is hoped that this research can be used to adjust the product layout according to the level of frequency the product is sought by the customer so that the customer has no difficulty finding the product they want.
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Totad, Shashikumar G., R. B. Geeta, and P. V. G. D. Prasad Reddy. "Batch incremental processing for FP-tree construction using FP-Growth algorithm." Knowledge and Information Systems 33, no. 2 (2012): 475–90. http://dx.doi.org/10.1007/s10115-012-0514-9.
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Xu, Fangqin, and Haifeng Lu. "The Application of FP-Growth Algorithm Based on Distributed Intelligence in Wisdom Medical Treatment." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 04 (2017): 1759005. http://dx.doi.org/10.1142/s0218001417590054.
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FP-Growth algorithm is an algorithm of association rules that does not generate a set of candidate, so it has very high practical value in face of the rapid growth of data volume in wisdom medical treatment. Because FP-Growth is a memory-resident algorithm, it will appear to be powerless when it is used for massive data sets. The paper combines Hadoop and FP-Growth algorithm and through the actual analysis of traditional Chinese medicine (TCM) data compares the performance in two different environments of stand-alone and distributed. The experimental results show that FP-Growth algorithm has a great advantage in the processing and execution of massive data after the MapReduce parallel model, so that it will have better development prospects for intelligent medical treatment.
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Joseph, Jismy, and Kesavaraj G. "Evaluation of Frequent Itemset Mining Algorithms-Apriori and FP Growth." International Journal of Engineering Technology and Management Sciences 4, no. 6 (2020): 1–4. http://dx.doi.org/10.46647/ijetms.2020.v04i06.001.
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Nowadays the Frequentitemset mining (FIM) is an essential task for retrieving frequently occurring patterns, correlation, events or association in a transactional database. Understanding of such frequent patterns helps to take substantial decisions in decisive situations. Multiple algorithms are proposed for finding such patterns, however the time and space complexity of these algorithms rapidly increases with number of items in a dataset. So it is necessary to analyze the efficiency of these algorithms by using different datasets. The aim of this paper is to evaluate theperformance of frequent itemset mining algorithms, Apriori and Frequent Pattern (FP) growth by comparing their features. This study shows that the FP-growth algorithm is more efficient than the Apriori algorithm for generating rules and frequent pattern mining.
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Lestari, Putrye Aufia Indah, and Nita Cahyani. "Application Of The Association Rule Method Based On Book Borrowing Patterns In Bojonegoro Regional Libraries." Journal of Computer Networks, Architecture and High Performance Computing 5, no. 2 (2023): 751–59. http://dx.doi.org/10.47709/cnahpc.v5i2.2893.
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The library is an institution that processes collections of written and printed works, to meet the educational, research, information, and recreation needs of its users. The Bojonegoro Library Service provides reading materials with a collection of around 24,130 book titles and around 24,130 book copies. The number of registered visitors was 1,424 people. From 2021-2022, there are 303 book lending transaction data. Knowing the results of the Association Rule with the Frequent Pattem-Growth algorithm in determining recommendations for book placement based on borrowing patterns in libraries in the Bojonegoro area. The method used is Association Rule Mining, to produce an efficient algorithm, the algorithm used is the Frequent Pattern Growth (FP-Growth) Algorithm. The characteristic of the FP-Growth algorithm is the data structure used in a tree called FP-Tree. By using FP-Tree the FP-Growth algorithm can directly extract frequent itemsets from FP-Tree. The results of the research carried out by applying the FP growth algorithm with a support value limit of 20% and a confidence value of 80% from a dataset of 144 book lending transactions which became frequent itemsets were a combination of itemsets, resulting in a strong rule of 5 association rules which met the requirements. Can help the Bojongoro Library and archives service to improve the quality of service and can provide recommendations for librarians and as a reference for placing classes of books that are more often borrowed together closer together.
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Gupta, Priyanka, and Vinaya Sawant. "A Parallel Apriori Algorithm and FP- Growth Based on SPARK." ITM Web of Conferences 40 (2021): 03046. http://dx.doi.org/10.1051/itmconf/20214003046.
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Frequent Itemset Mining is an important data mining task in real-world applications. Distributed parallel Apriori and FP-Growth algorithm is the most important algorithm that works on data mining for finding the frequent itemsets. Originally, Map-Reduce mining algorithm-based frequent itemsets on Hadoop were resolved. For handling the big data, Hadoop comes into the picture but the implementation of Hadoop does not reach the expectations for the parallel algorithm of distributed data mining because of its high I/O results in the transactional disk. According to research, Spark has an in-memory computation technique that gives faster results than Hadoop. It was mainly acceptable for parallel algorithms for handling the data. The algorithm working on multiple datasets for finding the frequent itemset to get accurate results for computation time. In this paper, we propose on parallel apriori and FP-growth algorithm to finding the frequent itemset on multiple datasets to get the mining itemsets using the Apache SPARK framework. Our experiment results depend on the support value to get accurate results.
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Yin, Ming, Wenjie Wang, Yang Liu, and Dan Jiang. "An improvement of FP-Growth association rule mining algorithm based on adjacency table." MATEC Web of Conferences 189 (2018): 10012. http://dx.doi.org/10.1051/matecconf/201818910012.
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FP-Growth algorithm is an association rule mining algorithm based on frequent pattern tree (FP-Tree), which doesn’t need to generate a large number of candidate sets. However, constructing FP-Tree requires two scansof the original transaction database and the recursive mining of FP-Tree to generate frequent itemsets. In addition, the algorithm can’t work effectively when the dataset is dense. To solve the problems of large memory usage and low time-effectiveness of data mining in this algorithm, this paper proposes an improved algorithm based on adjacency table using a hash table to store adjacency table, which considerably saves the finding time. The experimental results show that the improved algorithm has good performance especially for mining frequent itemsets in dense data sets.
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Faris Syaifulloh, Eva Yulia Puspaningrum, and M. Muharram Al Haromainy. "Analisis Pola Pembelian Pelanggan Menggunakan Algoritma Squeezer, Apriori dan FP-Growth Pada Toko Bangunan." Modem : Jurnal Informatika dan Sains Teknologi. 2, no. 3 (2024): 134–47. http://dx.doi.org/10.62951/modem.v2i3.153.
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To compete with other stores, store owners need to design various strategies, one of which is understanding customer purchase patterns. This article examines the Squeezer algorithm and compares the performance of the Apriori and FP-Growth algorithms in forming customer purchase association patterns that can be used as a reference for store owners in planning sales strategies. The data mining process was carried out using Association Rules and Clustering methods. A total of 1256 sales transaction data samples were analyzed to understand the association patterns produced by each method. Based on the test results with a minimum support of 0.2 and a confidence of 0.6, the Apriori algorithm produced 194 association rules with a total rule strength of 1.16. Meanwhile, the FP-Growth algorithm produced 52 association rules with the same total rule strength of 1.16. The Clustering Method resulted in 7 clusters with a similarity value of 0.06322. After comparison, the FP-Growth algorithm proved to have better performance in generating association rules compared to the Apriori algorithm.
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MRS., KIRAN TIKAR, and KAVITA SURYAWANSHI DR. "A COMPARATIVE STUDY OF ASSOCIATION RULE MINING ALGORITHMS." JournalNX - A Multidisciplinary Peer Reviewed Journal ICACTM (May 3, 2018): 78–80. https://doi.org/10.5281/zenodo.1410059.
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Data mining (DM) techniques is the set of algorithms that helps in extracting interesting patterns and previously unknown facts from larger volume of databases. Todays ever changing customer needs, fluctuation business market and large volume of data generated every second has generated the need of managing and analyzing such a large volume of data. Association Rule mining algorithms helps in identifying correlation between two different items purchased by an individual. Apriori Algorithm and FP-Growth Algorithm are the two algorithms for generating Association Rules. This paper aims at analyze the performance of Apriori and FP-Growth based on speed, efficacy and price and will help in understanding which algorithm is better for a particular situation. https://journalnx.com/journal-article/20150659
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Zhang, Xian Hong. "Research of Data Mining Algorithm Based on the Intrusion Prevention System." Applied Mechanics and Materials 644-650 (September 2014): 1787–90. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1787.
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With the rapid development of network, the security problem of network becomes an issue which has been paid more and more attentions to. Among so many methods of intrusion prevention, data mining is a very effective one. The FP-growth algorithm is the most widely used algorithm for mining frequent item-sets, which is also an algorithm for mining association rules without candidate set. However, the FP-growth algorithm needs large memory when mining large database,and its running speed is slow. In order to overcome these problems, based on the FP-growth algorithm, this paper proposed an optimized algorithm. This paper compared the new algorithm with the previous one based on intrusion prevention model for campus network by experiments. Based on Experiments, we can draw the conclusion that, mining association rules by using the improved FP-growth algorithm can effectively detect the users’ behavior pattern, historical pattern and the current model to calculate the similarity of users, and provides the possibility to accurately judge the user behavior.
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Dwiputra, Dedy, Agung Mulyo Widodo, Habibullah Akbar, and Gerry Firmansyah. "Evaluating the Performance of Association Rules in Apriori and FP-Growth Algorithms: Market Basket Analysis to Discover Rules of Item Combinations." Journal of World Science 2, no. 8 (2023): 1229–48. http://dx.doi.org/10.58344/jws.v2i8.403.
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This study focuses on applying data mining techniques, especially association rules mining using the Apriori and FP-GROWTH algorithms, for market basket analysis on PT. XYZ is a pharmaceutical company in Indonesia. A quantitative methodology uses a dataset of 100,498 transactions originating from 432,356 rows of data covering July to December 2022 in the JABODETABEK area. Apriori and FP-GROWTH algorithms are applied for association rules mining. The results show that FP-GROWTH has the fastest execution time of 84,655 seconds. However, the memory usage for the Apriori algorithm is the lowest at 482.32 MiB, with increments of: 0.21 MiB. For the rules generated, the two algorithms, both Apriori and FP-GROWTH, produce the same number of rules and values of support, confidence, lift, Bi-Support, Bi-Confidence, and Bi-Lift. In conclusion, Apriori is recommended for sales datasets if memory usage and ease of implementation are important. However, if the speed of execution time and a large amount of data are considered, FP-GROWTH is a better choice because the execution time is faster for large amounts of data. However, the choice of algorithm depends on the specific analysis objectives, itemset size, data scale, and computational capabilities. Results from association rules mining provide evidence of product popularity, purchasing patterns, and opportunities for strategic marketing and inventory management. These findings can help PT. XYZ improves business efficiency, understands customer behavior, and increases profitability.
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K. Manikandan, N., and D. Manivannan. "Frequent item set mining using normalized FP-growth algorithm." International Journal of Engineering & Technology 7, no. 1.7 (2018): 59. http://dx.doi.org/10.14419/ijet.v7i1.7.9573.
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As the volume of data and its storage schemes are increasing drastically, the knowledge discovery from these huge volume of heterogeneous and high dimension data emerges as an essential process. Number of algorithms for data association analysis has been introduced considering time and main memory requirements. However this algorithms get completed when the items and records grows extremely high. In this paper we have created a data structure that can be reused without modifying the schema. So the aim of this work is to make an efficient association rule mining independent of the algorithm being selected.Our data structure make data access much faster by simplifying and reorganizing them by implementing shuffling strategy using hamming distance and inverted index mapping. In this work we explore our algorithm’s efficiency by using many datasets containing millions of records in it. We increased the runtime orders of the magnitude and reduced the auxiliary and main memory requirements.
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K. Manikandan, N., and D. Manivannan. "Frequent item set mining using normalized FP-growth algorithm." International Journal of Engineering & Technology 7, no. 1.8 (2018): 59. http://dx.doi.org/10.14419/ijet.v7i1.8.9572.
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As the volume of data and its storage schemes are increasing drastically, the knowledge discovery from these huge volume of heterogeneous and high dimension data emerges as an essential process. Number of algorithms for data association analysis has been introduced considering time and main memory requirements. However this algorithms get completed when the items and records grows extremely high. In this paper we have created a data structure that can be reused without modifying the schema. So the aim of this work is to make an efficient association rule mining independent of the algorithm being selected.Our data structure make data access much faster by simplifying and reorganizing them by implementing shuffling strategy using hamming distance and inverted index mapping. In this work we explore our algorithm’s efficiency by using many datasets containing millions of records in it. We increased the runtime orders of the magnitude and reduced the auxiliary and main memory requirements.
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Bashir, Shariq, and Daphne Teck Ching Lai. "Mining Approximate Frequent Itemsets Using Pattern Growth Approach." Information Technology and Control 50, no. 4 (2021): 627–44. http://dx.doi.org/10.5755/j01.itc.50.4.29060.
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Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.
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Xu, Rui. "The Evaluation of Ethnic Costume Courses based on FP-growth Algorithm." Scalable Computing: Practice and Experience 25, no. 1 (2024): 313–26. http://dx.doi.org/10.12694/scpe.v25i1.2297.
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In order to make full use of the accumulated curriculum data of Folk costume and dig out useful information from it, so as to provide useful information for curriculum teaching, the article proposes three general functions based on the requirement analysis, and pre-processes the completed grade data of ethnic costume students in 4 academic years, analyzes these data by FP-growth algorithm to understand the situation of association rules between different courses, and through K-means++ algorithm The clustering analysis of students with different levels of achievement is carried out and the results are validated by examples. In the algorithm performance analysis, the performance of FP-growth algorithm is better, the average absolute error of FP-growth algorithm is always smaller than that of Apriori algorithm; When the support degree is 20%, the running time of FP-growth algorithm is 0.4s, which is 0.4s less than that of Apriori algorithm. when the number of calculation nodes is 5, the running time of FP-growth algorithm and the accuracy of the K-means++ algorithm were higher than that of the K-means algorithm. In the Iris dataset, the accuracy of the K-means++ algorithm was 91.05%, which was 8.94% higher than that of the K-means algorithm. When mining the course grade data, the confidence level of the obtained association rules was even higher, even up to 97.15%. The standardized test score for the second group of students was 0.960. The course evaluation method used in the article was more objective and the accuracy of the data analysis was higher, providing valuable reference information for teachers' teaching.
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Yogasuwara, Rangga, and Ferdiansyah Ferdiansyah. "Implementasi Algoritma Frequent Growth (FP-Growth) Menentukan Asosiasi Antar Produk." Jurnal Sistem Komputer dan Informatika (JSON) 4, no. 1 (2022): 165. http://dx.doi.org/10.30865/json.v4i1.4894.
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Data accumulation is caused by the amount of transaction data stored. By utilizing the sales transaction data in the database, the data can be further processed into useful information for managers to make decisions. With the existence of data mining, it is hoped that it can help the Leaning Shop to find the information contained in the transaction data into new knowledge. Association Rule, which is a procedure in Market Basket Analysis to find relationships between items in a data set or it can be said that this association rule aims to find a collection of items that often appear at the same time and display them in the form of consumer habits in shopping. The FP-Growth algorithm is an algorithm that can be used to determine the data set that appears most often (frequent itemset) in a data, in the search for frequent itemset in a data set by generating a prefix-tree structure or often called the FP-Tree. From the test results it can be concluded that the application of data mining using the FP-Growth Algorithm can be used to analyze consumer spending patterns.
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Singh, Archana, Jyoti Agarwal, and Ajay Rana. "Performance Measure of Similis and FP-Growth Algorithm." International Journal of Computer Applications 62, no. 6 (2013): 25–31. http://dx.doi.org/10.5120/10085-4712.
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Wang, Xinyan, and Guie Jiao. "Research on association rules of course grades based on parallel FP-Growth algorithm." Journal of Computational Methods in Sciences and Engineering 20, no. 3 (2020): 759–69. http://dx.doi.org/10.3233/jcm-194079.
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With the rapid growth of massive data in all walks of life, massive data faces enormous challenges such as storage capacity and computing power. In Chinese universities, traditional data analysis of student course cannot meet the growing demand for increasing data size and real-time computation of big data. In this paper, a parallel FP-Growth algorithm based on split is proposed. The established FP-Tree is split into blocks, and the split FP-Trees are equally divided into different nodes. The monitoring point is set up to monitor the operation of other nodes, dynamically migrate tasks and maintain load balancing. The experiment proves that each node has good load balancing with the given support degree, and the improved algorithm has better running performance than the classic FP-Growth algorithm in parallel processing. Finally, the parallel FP-Growth algorithm based on split is implemented on Hadoop to mine association rules between course grades. The mining process includes data preprocessing, mining results and analysis. The association rules between course grades provide suggestions for the way students learn and the way teachers teach.
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Yuanyuan, Li, and Yin Shaohong. "Mining Algorithm for Weighted FP-Growth Frequent Item Sets based on Ordered FP-Tree." International Journal of Engineering and Management Research 9, no. 5 (2019): 154–58. https://doi.org/10.31033/ijemr.9.5.22.
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FP-growth algorithm is a classic algorithm of mining frequent item sets, but there exist certain disadvantages for mining the weighted frequent item sets. Based on the weighted downward closure property of the weighted model, this paper proposed a method to reduce the use of storage space by constructing a weight ordered FP-tree, so as to improve the generation efficiency of weighted frequent item sets.
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Damanik, Florida Nirma Sanny, Andrew Sagita, Harianto -, and Andy Syaputra. "Aplikasi Pengenalan Pola Pembelian Konsumen Menggunakan Kombinasi Algoritma FP-Growth Dan ECLAT Method (FEM)." Jurnal SIFO Mikroskil 19, no. 2 (2018): 1–12. http://dx.doi.org/10.55601/jsm.v19i2.553.
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Sales data stored in enterprise databases are usually stored as archives or documentation. In the case of retail companies, data mining science can be used to extract new information from sales database, ie consumer purchase pattern analysis. The algorithm that can be used to analyze consumer purchase pattern is FEM algorithm using combination of Frequent Pattern Growth (FP-Growth) and Eclat algorithm. The construction of FP-Tree tree structure is done by using FP-Growth algorithm, while the process of extraction of items purchased (frequent itemset) is done by using Eclat algorithm. The application designed can be used to analyze consumer purchase pattern by generating associative rules using FEM algorithm through the Analysis form and printing the consumer purchase pattern through the purchase pattern report.
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Robi, Nana Suarna, Irfan Ali, and Dendy Indriya Efendi. "Identify Rattan Sales Patterns Using the FP-Growth Algorithm on CV. Busaeri Rattan." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 1246–55. https://doi.org/10.59934/jaiea.v4i2.877.
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This research was conducted to recognize the pattern of purchasing rattan products at CV. Busaeri Rattan by utilizing the FP-Growth algorithm. The rattan industry is faced with the challenge of understanding consumer habits in order to improve marketing strategies. The FP-Growth algorithm was chosen for its ability to efficiently identify frequent itemset patterns without requiring a lot of memory. This research includes collecting rattan sales transaction data for one year, data preprocessing, FP-Tree structure formation, and frequent itemset analysis. The analysis was conducted using RapidMiner software with a minimum support setting of 0.005 and confidence of 0.1. The processed data was then used to find combinations of products that are often purchased together. The results revealed some significant patterns, such as the products “Mandola 3/4” and “Jawit 8/11,” which are often purchased together with a confidence level of 100%. These findings provide important insights for CV. Busaeri Rattan in increasing sales through promotional strategies such as bundling or discount offers. In addition, the FP-Growth algorithm proved to be faster and more resource-efficient than traditional methods such as Apriori. The discussion shows that the discovered purchasing patterns can help CV. Busaeri Rattan better manage stock, minimize the risk of running out of goods, and design data-driven marketing strategies. The combination of products that are often purchased together can be utilized to improve customer satisfaction as well as operational efficiency. The conclusion of this research is that the FP-Growth algorithm is an effective tool for analyzing large-scale transaction data. Further research is recommended to explore the application of this algorithm to other types of products or compare it with other data mining algorithms.
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Li, J. W., N. Yu, J. W. Jiang, X. Li, Y. Ma, and W. D. Chen. "RESEARCH ON STUDENT BEHAVIOR INFERENCE METHOD BASED ON FP-GROWTH ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 981–85. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-981-2020.
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Abstract. How to use modern information technology to efficiently and quickly obtain the personalized recommendation information required by students, and to provide high-quality intelligent services for schools, parents and students has become one of the hot issues in college research. This paper uses FP-growth association rule mining algorithm to infer student behavior and then use the collaborative filtering recommendation method to push information according to the inference result, and then push real-time and effective personalized information for students. The experimental results show that an improved FP-growth algorithm is proposed based on the classification of students. The algorithm combines the student behavior inference method of FP-growth algorithm with the collaborative filtering hybrid recommendation method, which not only solves the FP-tree tree branch. Excessive and collaborative filtering recommendation algorithm data sparseness problem, can also analyze different students' behaviors and activities, and accurately push real-time, accurate and effective personalized information for students, to promote smart campus and information intelligence The development provides better service.
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A, Senthilkumar, and Hari Prasad D. "An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce." Indian Journal of Science and Technology 13, no. 34 (2020): 3561–71. https://doi.org/10.17485/IJST/v13i34.1078.
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Abstract <strong>Objectives:</strong> To achieve improved performance of FP-Growth based Association Rule Mining algorithm for massive data by effective utilization of storage,execution capability and improved partition technique within the Hadoop MapReduce framework. <strong>Methodology:</strong> The proposed methodology has four main phases: In the first phase, the item sets for finding the frequent pattern are encoded and thus minimizes the expensive operation for large data set. In the second phase, improved hash partitioning reduces the network overhead and improves the communication speed within the MapReduce phase for each item set. The effective usage of network bandwidth and storage is obtained by the impact of compression in the third phase. The use of combiner in final phase for frequent item set mining minimizes the overhead of reduce phase by finding the pattern in each partition and minimizes the overall execution time of the FP-Growth algorithm. <strong>Findings:</strong> FP-Growth based association rule mining algorithm is designed for parallel execution on distributed cluster of servers. Changes to the MapReduce implementation of FP-Growth with the impact of encoding. Improved hash partitioning, compression and configuration results in a significant performance gain with better improvement in execution time.<strong>Novelty/Improvements:</strong> According to the experimental results, the changes in storage and processing level within the MapReduce framework improves the overall performance of the parallel frequent item set mining in Hadoop cluster. <strong>Keywords:</strong> Association rule mining; Hadoop; MapReduce; FP-Growth
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Annisah, Yessi Fitri, and Siti Sundari. "Implementasi Association Rules dengan Algoritma FP - Growth." Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) 6, no. 1 (2025): 42–53. https://doi.org/10.35447/jikstra.v6i1.1071.
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This research aims to help Ayaa Fashion Store improve its sales strategy by understanding consumer shopping patterns. This research was conducted by utilizing the FP-Growth algorithm to analyze consumer purchasing patterns based on sales transaction data at the Ayaa Fashion Store which operates in the sales of women's clothing, mukenas and headscarves. The data collection method is carried out through daily sales transaction information, with a total of 365 transactions recording goods purchased by consumers. The use of the FP-Growth Algorithm allows identifying events that frequently occur in the dataset, producing association rules that describe consumer shopping patterns. The analysis was carried out by paying attention to the support and confidence values, and using the lift ratio to assess the strength of the association rules. Data processing was carried out using RapidMiner software. The research results include 12 association rules and 20 itemsets of goods that consumers frequently buy. The results of this research evaluation show that the FP-Growth algorithm is effective in analyzing sales transaction data, providing valuable guidance for designing sales strategies, and optimizing the supply of goods for Ayaa Fashion Stores.
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Abdullah, Zailani, Tutut Herawan, A. Noraziah, and Mustafa Mat Deris. "A Scalable Algorithm for Constructing Frequent Pattern Tree." International Journal of Intelligent Information Technologies 10, no. 1 (2014): 42–56. http://dx.doi.org/10.4018/ijiit.2014010103.
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Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.
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Hartanti, Dwi, and Vihi Atina. "Product Stock Supply Analysis System with FP Growth Algorithm." Journal of Information Systems and Informatics 5, no. 4 (2023): 1312–20. http://dx.doi.org/10.51519/journalisi.v5i4.580.
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This study explores the application of Data Mining in deciphering consumer purchasing patterns at Tani Heritage Shop, a retailer specializing in agricultural products. Facing the challenge of managing a high volume of daily sales transactions, the shop often encounters difficulties in tracking which products are frequently purchased together. This lack of insight leads to a critical issue: popular products running out of stock unexpectedly. To address this, the research focuses on developing a product stock supply analysis system, utilizing the FP Growth Algorithm. The FP Growth Algorithm, a powerful tool in Data Mining, is employed to analyze sales transaction data and identify consumer purchasing trends, particularly products bought simultaneously. This approach is designed to provide insights into optimal stocking strategies, ensuring the availability of in-demand products. The research methodology involves applying the FP Growth Algorithm to model the product stock supply system, using specific sales data attributes. The results of this study are significant. By setting parameters such as a minimum support value of 30%, a confidence value of 70%, and targeting the highest lift ratio value of 3.67, the research successfully derives several key association rules from the FP Growth algorithm. These rules are instrumental in optimizing the product stock supply analysis system.
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Mulya, DIo Prima. "ANALISA DAN IMPLEMENTASI ASSOCIATION RULE DENGAN ALGORITMA FP-GROWTH DALAM SELEKSI PEMBELIAN TANAH LIAT (STUDI KASUS DI PT. ANVEVE ISMI BERJAYA)." Jurnal Teknologi Dan Sistem Informasi Bisnis 1, no. 1 (2019): 47–57. http://dx.doi.org/10.47233/jteksis.v1i1.6.
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Data Mining aims to draw abstract knowledge of a big database.Data Mining also known as Knowledge Discovery Database. FP-Growth algorithm is one of the very popular algorithms in finding frequent itemset in finding the rule of a large data base. Association rule used to find patterns in market basket analysis. Steps in the process of association rule mining is confidence and support. Clay formed from the weathering of silica by carbonic acid and partly generated by geothermal activity. In this study using FP-Growth Algorithm in purchasing decisions withdrawal clay by PT. ISMI ANVEVE BERJAYA
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Xia, Dawen, Xiaonan Lu, Huaqing Li, Wendong Wang, Yantao Li, and Zili Zhang. "A MapReduce-Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data." Complexity 2018 (2018): 1–16. http://dx.doi.org/10.1155/2018/2818251.
Full textAbstract:
Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in data-driven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.