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1
He, Yue Shun, and Jun Fang Xiao. "Improved Methods on Association Rules Mining Algorithms." Key Engineering Materials 460-461 (January 2011): 148–52. http://dx.doi.org/10.4028/www.scientific.net/kem.460-461.148.
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Among the many mining algorithms of association rules, Apriori Algorithm is a classical algorithm that has caused the most discussion; it can effectively carry out the mining association rules. However, based on Apriori Algorithm, most of the traditional algorithms exist "item sets generation bottleneck" problem, and are very time-consuming. An enhanced algorithm associating Apriori with transaction reduction and item reduction technique is put forward by the paper, in the algorithm candidate item sets generation and the support calculation are created after each transaction is compressed and connected, and the key word identifying is adopted in the candidate set, thus the process of pruning and string pattern matching is removed from Apriori algorithm. Original algorithm and improved algorithm implementation steps are presented by examples, the results show that the new algorithm reduces the storage space, improve the efficiency of the algorithm and improve the performance of data mining technology.
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Tong, Yu Jun, Jun Zhou, Wen Ge Xie, and Dan Jia. "Research and Application of an Enhanced Data Mining Algorithm in Virtual Manufacturing Technology." Advanced Materials Research 299-300 (July 2011): 840–43. http://dx.doi.org/10.4028/www.scientific.net/amr.299-300.840.
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Association rules mining is an important branch of data mining. Apriori algorithm is a classical algorithm of mining association rules. Based on the original Apriori algorithm an improved Apriori algorithm is analyzed according to the multiple minimum supports and support difference constraint. An experiment has been conducted and the results showed that the new algorithm can not only mine out the association rules to meet the demands of multiple minimum supports, but also mine out the rare but potentially profitable items’ association rules.
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Wang, Pei Ji, and Yu Lin Zhao. "Research on Data Mining Based on Apriori Algorithm." Advanced Materials Research 532-533 (June 2012): 1675–79. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1675.
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With the availability of inexpensive storage and the progress in data collection tools, many organizations have created large databases of business and scientific data, which create an imminent need and great opportunities for mining interesting knowledge from data.Mining association rules is an important topic in the data mining research. In the paper, research mining frequent itemsets algorithm based on recognizable matrix and mining association rules algorithm based on improved measure system, the above method is used to mine association rules to the students’ data table under Visual FoxPro 6.0.
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Chauhan, Harvinder, and Anu Chauhan. "Implementation of the Apriori algorithm for association rule mining." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 04 (2014): 699–701. https://doi.org/10.5281/zenodo.14715587.
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With massive amounts of data continuously being collected and stored, many industries are becoming interested in mining association rules from their databases. The discovery of interesting association relationships among huge amounts of business transaction records can help in many business decision mak ing processes. Association rule mining contains some set of algorithms, whenever we mine the rules we have to use the algorithms. Weka, a software tool for data mining tasks contains the famous algorithm known as Apriori algorithm for association rule mining which computes all rules that have a given minimum support and exceed a given confidence. In this paper we are implementing Apriori algorithm using “weather data set” from weka. This paper also gives insights into the association rules mined by this algorithm in the implementation section.
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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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Johan, Ragil Andika, Rispani Himilda, and Nadya Auliza. "PENERAPAN METODE ASSOCIATION RULE UNTUK STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI." Jurnal Teknik Informatika (J-Tifa) 2, no. 2 (2019): 1–7. http://dx.doi.org/10.52046/j-tifa.v2i2.268.
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Abstrak
 Persaingan dalam bisnis khususnya dalam bisnis perdagangan semakin banyak. Agar dapat meningkatkan penjualan produk yang dijual, para pelaku harus mempunyai strategi. Salah satu cara yang bisa dilakukan adalah dengan memanfaatkan data transaksi penjualan. Data penjualan tersebut dapat diolah hingga didapatkan informasi yang berguna bagi peningkatan penjualan. Teknologi yang dapat digunakan dalam hal ini adalah data mining. Data mining adalah kegiatan pengolahan data untuk menemukan hubungan dalam suatu data yang berjumlah besar. Suatu metode yang dapat digunakan dalam data mining adalah association rule mining. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Algoritma yang paling sering dipakai dalam metode ini salah satunya ialah algoritma apriori. Algoritma apriori digunakan untuk mencari kandidat aturan asosiasi. Aturan kombinasi produk berhasil ditemukan dengan penerapan metode assosiation rules menggunakan algoritma apriori dan telah diuji menggunakan tools tanagra. Semua rule yang dihasilkan pada penelitian ini memiliki nilai lift ratio lebih dari 1 sehingga dapat digunakan sebagai acuan dalam membuat strategi penjualan.
 Kata Kunci : Penjualan, Data Mining, Association Rule, Algoritma Apriori
 
 Abstract
 Competition in business, especially in the trading business more and more. In order to increase sales of the products, businessman must have a strategy. A things we can do is to use sales transaction data. The sales data can be processed so we will get information of increasing sales. The technology that can be used in this case is data mining. Data mining, often also called knowledge discovery in database (KDD), is a data processing activity to find relationships in a large amount of data. A method that can be used in data mining is association rule mining. Association rule mining is one method of data mining that can identify the similarity relationships between items. One of the most frequently used algorithms in this method is the apriori algorithm. Apriori algorithm is used to find candidate association rules. The product combination rules have been found by applying the association rules method using apriori algorithm and have been tested using tanagra tools. All rules produced in this study have a lift ratio value of more than 1 so it can be used as a reference in making sales strategies.
 Keywords: Sale, Rule Mining, Association Rule, Apriori Algorithm
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Ji, Hai Peng, Tai Yong Wang, Jing Liu, Shi Yan Fan, Zhi Peng Wang, and Kai Ran Zhang. "An Efficient Parallel Association Rules Mining Algorithm for Fault Diagnosis." Key Engineering Materials 693 (May 2016): 1326–30. http://dx.doi.org/10.4028/www.scientific.net/kem.693.1326.
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With the development of Internet industry, equipment data is increasing. The traditional method is not suitable for processing large data. Aiming at inefficient problem of Apriori algorithm when mining very large database, an efficient parallel association rules mining algorithm (Advanced Pruning Parallel Apriori Algorithm) based on a cluster is presented. APPAA algorithm can enhance the mining efficiency, as well as the system’s extension. Experimental results show that APPAA algorithm cuts down 85% mining time of Apriori, and it has good characteristics of parallel and expandable.so it is suitable for mining very large size database of fault diagnosis.
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Yan, Chun, Jiahui Liu, Wei Liu, and Xinhong Liu. "Research on automobile insurance fraud identification based on fuzzy association rules." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 5821–34. http://dx.doi.org/10.3233/jifs-201301.
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With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.
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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.
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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
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Rakhimova, L.S. "PERFORMANCE ANALYSIS OF ASSOCIATION RULE MINING ALGORITHMS USING HADOOP." EURASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES 2, no. 14 (2022): 43–47. https://doi.org/10.5281/zenodo.7478791.
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Association rule mining has been a very important method in the field of data mining. Apriori algorithm is a classical algorithm for association rule mining. In the big data environment, the traditional Apriori algorithm has been unable to meet the needs of mining. In the paper, the parallelization of the Apriori algorithm is implemented based on the Hadoop platform and the Map Reduce programming model. On the basis, the algorithm is further optimized by using the idea of transaction reduction. Experimental results show that the proposed algorithm can be better to meet the requirements of big data mining and efficiently mining frequent itemsets and association rules from large dataset.
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Patil, Sonal, and Harshad Patil. "Secure Mining of Association Rules in Horizontally Distributed Databases." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 03 (2014): 663–67. https://doi.org/10.5281/zenodo.14715540.
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We propose a protocol for secure mining of association rules in horizontally distributed databases. Our protocol is optimized than the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. The main purpose of our protocol is to remove the problem of mining generalized association rules that affects the existing system. Our protocol offers more enhanced privacy with respect to previous protocols. In addition, it is simpler and is optimized in terms of communication rounds, communication cost and computational cost than other protocols .
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Chandrashekar, D. K., K. C. Srikantaiah, and K. R. Venugopal. "Map Reduce Based Association Rule Mining from Big Data." Journal of Computational and Theoretical Nanoscience 17, no. 9 (2020): 4262–66. http://dx.doi.org/10.1166/jctn.2020.9059.
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In today’s world, the shopping is the largest fashionable trend where the transaction processing is meticulous to fetch the items from the shopping transaction history by using traditional Apriori algorithm. An Apriori algorithm is the one which is used for finding frequent pattern from the given dataset. The problem of Apriori is to find useful itemsets for business purpose was time consuming. To overcome this problem, we have proposed Map Reduce based Apriori algorithm which generates frequent itemset and association rules by using parallel computations to reduce computations. The Spark distributed systems along with data bricks technology have been used. The experimental result shows that have been reduced the time taken fetch the data from the database.
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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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Wang, Bin Li, and Yan Guang Shen. "Improvement of Apriori Algorithm Based on Boolean Matrix." Advanced Materials Research 159 (December 2010): 144–48. http://dx.doi.org/10.4028/www.scientific.net/amr.159.144.
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This paper introduces the association rules and Apriori algorithm in data mining, considering the disadvantages of Apriori algorithm, a new improved Apriori algorithm based on Boolean matrix is proposed .It scans transaction database only one time, thus reduces the system cost and increases efficiency of data mining.
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Wang, Ping Shui. "A New Algorithm of Association Rules Mining Based on Relation Matrix." Advanced Materials Research 179-180 (January 2011): 55–59. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.55.
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Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.
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Gama, Adie Wahyudi Oktavia, and Ni Made Widnyani. "Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique." Knowledge Engineering and Data Science 3, no. 2 (2020): 89. http://dx.doi.org/10.17977/um018v3i22020p89-98.
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Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;
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Ye, Yi Yong. "Research and Application of Apriori Algorithm for Mining Association Rules." Advanced Materials Research 1079-1080 (December 2014): 737–42. http://dx.doi.org/10.4028/www.scientific.net/amr.1079-1080.737.
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For large amounts of data generated by the e-commerceplatform, combining with the actual needs of e-commerce recommendation system,make research on a common technique of association rules which orientede-commerce Web mining association analysis, introduces the association rules ofApriori mining algorithm, and the specific application of Apriori algorithm isanalyzed through a practical example, Finally, point out the shortcomings ofclassical Apriori algorithm, and gives directions for improvement.
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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.
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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.
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Aldino, Aldino. "PENERAPAN ALGORITMA ECLAT DAN APRIORI PADA DATA MINING UNTUK MARKET BASET ANALISIS PENJUALAN." Jurnal Data Mining dan Sistem Informasi 3, no. 2 (2022): 28. http://dx.doi.org/10.33365/jdmsi.v3i2.2207.
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Growth of the retail business makes competition in implementing better marketing strategies. This research aims to analyze the shopping basket or market basket analysis in a mini market. Using two algorithms, namely the Eclat algorithm and the Apriori algorithm to analyze sales data, the purpose of this study is to find out the best algorithm in finding association rules or association rules from sales data and provide information regarding what items are the most sold as well as to find out what items. which must be displayed on the sales shelf at the same time. Based on the results of the implementation of thealgorithms Eclat and Apriori concluded that the algorithm Eclat works better thanalgorithm Apriori can be seen from the process of seeking the rule of 212 Mart sales data, Eclat algorithm produces a rule as much as 86 items with a time of 0.01s.
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Dharma, Teguh Satya, and I. Ketut Gede Suhartana. "Polyclinic Visitor Pattern Discovery Using Apriori Algorithm." JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) 9, no. 2 (2020): 229. http://dx.doi.org/10.24843/jlk.2020.v09.i02.p09.
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Living in an information era, the presence of data is super important. With the data exponentially grows from decades to decades, it reflect the old saying that people today are so rich with data yet so poor on information. To dissect the information that contained within the large amount of data, a method is introduced called “Data Mining.” Data mining is a process of retriving a unique, unseen, and valuable information/insight from the data. Data mining comes with a lot of methods branches, one of those is pattern analysis, or also known as “Association Rules”. With the help of Association Rules, people can discover the relational pattern within the data, so people can make the best decision on the period of time. In this study, the writer is implementing Apriori Algorithm (one of the Association Rules algorithm) to see the pattern of a polyclinic visitor.
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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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Bicer, Mehmet, Daniel Indictor, Ryan Yang, and Xiaowen Zhang. "Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm." International Journal of Applied Logistics 11, no. 1 (2021): 18–37. http://dx.doi.org/10.4018/ijal.2021010102.
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Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.
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.Naveenkumar, Dr R. "Deciphering Patterns in a small scale Case Analysis Study of the Apriori Algorithm in Market Basket Analysis using machine learning tools." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01603.
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Analysis of Transaction Database Using Apriori Algorithm. This study examines the use of the Apriori algorithm for analysing transaction databases. The Apriori algorithm is a fundamental technique in data mining that allows for the efficient discovery of frequent patterns and association rules in large datasets. The Apriori algorithm employs a two-step approach. Initially, it identifies frequent items in the database based on a user-defined minimum support threshold. Subsequently, it generates association rules that describe relationships between these frequent items based on metrics such as confidence and lift. This paper provides an in-depth explanation of the Apriori algorithm, emphasizing its strengths and limitations. Additionally, it presents various applications of the Apriori algorithm in real-world scenarios, such as shopping cart analysis, cross-selling and upselling, and customer segmentation. The significance of this study lies in its comprehensive analysis of the Apriori algorithm and its practical relevance in diverse data mining tasks. It serves as a valuable resource for researchers, practitioners, and anyone seeking to understand the fundamentals and applications of association rule mining. Keywords: Datamining, OLTP, Apriori algorithm, Item sets and Market Basket Analysis
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Lubis, Anju Eliarsyam, and Paska Marto Hasugian. "Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method." Journal Of Computer Networks, Architecture and High Performance Computing 2, no. 1 (2020): 23–29. http://dx.doi.org/10.47709/cnapc.v2i1.353.
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The sale is part of the marketing that determine the survival of the company. With the sale, the company can achieve the goals or targets. To be a company that continues to grow in motorcycle sales, the company should be able to compete in increasing sales volume. Starting from the launch prodak the best in sophistication motorcycles, up to a very attractive price cuts the attention of consumers. Things like that already sanggat often do, so the company can still compete, Motorcycles is a two-wheeled transfortasi tool used more and more common people. From teenagers to old orag, not infrequently motorcycle including important sanggat needs. If we do not have it feels very hard in activity quickly. Make sales without any restriction of sales data accumulate, until finally overwhelmed the company in terms of taking care of customer files. To find the most sales required Apriori Algorithm. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules.
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Zhang, Qian. "The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities." Applied Mechanics and Materials 321-324 (June 2013): 2578–82. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2578.
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This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.
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Zakur, Yahya, and Laith Flaih. "Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review." E3S Web of Conferences 448 (2023): 02021. http://dx.doi.org/10.1051/e3sconf/202344802021.
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Data mining has the potential to empower healthcare organizations by allowing them to analyze various aspects of patient information and discover connections between seemingly unrelated data. By harnessing advanced data analysis techniques, healthcare providers can identify trends in patients' medical conditions and behaviours. The Apriori algorithm is used for mining frequent item sets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability, while Apriori-Hybrid. Apriori-Hybrid is the combination of algorithms Apriori and Apriori-TID, which can classify large itemsets and can improve the accuracy of classification and it can also shed light on the basic mechanism. In this research, a comparison was made between the two algorithms in terms of capabilities, strengths, areas of use, and suggestions about the nature of using each algorithm.
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Wang, Xiaoli, Kui Su, and Lirong Su. "Research on Improved Apriori Algorithm Based on Data Mining in Electronic Cases." International Journal of Healthcare Information Systems and Informatics 14, no. 3 (2019): 16–28. http://dx.doi.org/10.4018/ijhisi.2019070102.
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This article makes progress of a commonly used Apriori algorithm, and proposes a new Apriori algorithm based on event ID. In this article, association rules are gained from massive medical data through the new Apriori algorithm. This article proposes and then uses the association rules in the prediction system. This article aims at making the lifestyle-related diseases prediction system provide better service for people, for families and for the whole society. The prediction system can automatically give out health-related information of the user after the person's basic information is put in, and it would also give out some pieces of valuable advice according to the resultant data, helping people realize self-determinant health engagement.
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Mohammad, Marufuzzaman, Gomes D., A. A. Rupai A., and M. Sidek L. "Discovering rules for nursery students using apriori algorithm." Bulletin of Electrical Engineering and Informatics 9, no. 1 (2020): 298–303. https://doi.org/10.11591/eei.v9i1.1665.
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Over recent years, there has been a rise in the number of students completing nursery education in Bangladesh. However, in order to achieve a sustainable education goal, the dropout rate in education needs to be reduced. Therefore, this research worked on providing insights that would help to understand the possible causes of dropout from education. Since primary education is the starting point for every student, this research has been conducted on this part of education. The research used data obtained from a European country, Slovenia to use the insights of a developed country. The study was conducted using association rule mining where several mining rules were generated using the Apriori algorithm. The rules obtained had the confidence of 0.95 and support of 0.04. The result showed three major rules of dropping out children in nursery education and eventually helps to ensure higher education for all children.
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Zheng, Yi, Peipei Chen, Biyu Chen, Dengjun Wei, and Meifang Wang. "Application of Apriori Improvement Algorithm in Asthma Case Data Mining." Journal of Healthcare Engineering 2021 (November 1, 2021): 1–7. http://dx.doi.org/10.1155/2021/9018408.
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In Chinese medicine, asthma cases contain a large amount of empirical data which are obtained from the clinical diagnosis of doctors throughout the year. Data correlation analysis method is among the common mechanisms which are used to mine association between the (1) prescriptions and prescribers (doctors in this case) and (2) symptoms and medications for a particular disease in the hospitals. In this paper, initially, a thorough analysis of expected performance and shortcomings of the Apriori algorithm in mining of medical case data is presented. Secondly, we propose an extended version of the traditional Apriori algorithm which is primarily based on the fast response of computer to bit-string logic operation. A comparative evaluation of the proposed and existing Apriori algorithms is presented particularly in terms of running time, mining of frequent items set and strong association rules. Both experimental and simulation results have proved that the proposed extended Apriori algorithm has outperformed existing algorithms when it is applied to asthma medication and combined symptom-medication data for the association analysis. Furthermore, the association relationship between mind asthma case data and medication is effective in the analysis of asthma case data with significant application value which is verified by the experimental data and observations.
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Zhang, Shu Juan, and Qing Min Wang. "The Research and Application of Association Rules Algorithm." Applied Mechanics and Materials 325-326 (June 2013): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1623.
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Through the research of the association rules mining technology and Apriori algorithm, the defects are found in Apriori algorithm. In view of the deficiencies, an improved algorithm is proposed. The algorithm scans database only once, and efficiently reduces the I/O time. The matrix of frequent itemsets is used to store and reduce the transaction data, which saves the storage space. By comparison of Apriori algorithm and improved algorithm, the results of experiments show that the efficiency of the improved algorithm is increased. Finally, an application example of the association rules is given. The improved algorithm is introduced to book lending deal. The rules among the book-borrowed are discovered and analyzed.
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Femi, Dwi Astuti, and Andriyani Widyastuti. "OPTIMASI PEMROGRAMAN QUERY UNTUK ALGORITMA APRIORI BERBASIS ASOSIASI DATA MINING." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2016): 1–13. https://doi.org/10.5281/zenodo.546756.
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Generally, each measurement is associated with a threshold value value can be determined by the user data mining. The association rules do not use a threshold tend to be interesting because it does not represent knowledge to users of data mining. One factor that gives contributions to determine whether a pattern is interesting or not simplicity in human understanding. The more complex the structure of a rules, it is increasingly difficult to be interpreted so that the pattern is formed increasingly unattractive.
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Malik, C. K. Mohammed. "Web Mining Using Improved Apriori Algorithm." International Academic Journal of Innovative Research 9, no. 1 (2022): 52–60. http://dx.doi.org/10.9756/iajir/v9i1/iajir0917.
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In this study, we will be concentrating on one of the more recent advancements in data mining, specifically mining online usage. The purpose of web use mining is to gain usable knowledge from the data that web servers keep about the actions of its visitors by mining the data that is stored on such servers. By using the association rule generation in the Web domain, the pages that are most frequently referenced together can be combined into a single server session. This is possible because of the interconnected nature of the Web. In association rule mining, a technique known as frequent set mining is one of the methods that may be used to discover regular patterns from a web log file. When it comes to mining the usage of the web, the term association rules refers to groups of web pages that are accessed together and have a support value that is higher than a given threshold. The support can be expressed as a proportion of total transactions that match a particular pattern. With the aid of the presence or absence of association rules, web designers are able to effectively reconstruct the websites they have created for their clients. In this research, we have introduced a method called Aprior for the purpose of extracting frequent patterns from online log files. The findings of the experiments that were carried out on data relating to peoples use of the website indicate that general sequential patterns or frequent item sets are more suitable for use in Web customization and recommender systems.
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Zhang, Ya Ni. "The Design and Analysis of the Information Management System Based on Data Mining." Applied Mechanics and Materials 687-691 (November 2014): 1308–11. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1308.
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This paper studies on the data mining technology based on association rules, and analyzes on important algorithm in association rules - the advantages and disadvantages of Apriori algorithm and puts forward an improved Apriori-mapping algorithm based on address mapping. This algorithm adopts the way of horizontal deposit transaction, establishes candidate item identification list of corresponding candidate project transaction and length value of transaction list. And shorten the pruning operation time by address mapping, and compress the frequent item sets number of operation connected operation with large amplitude.The system efficiency is improved, and the performance of the algorithm has been improved by experiment.
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Xiong, Ye Qing, and Shu Dong Zhang. "Research of Association Rules Algorithm Based on Matrix under Cloud Computing." Applied Mechanics and Materials 568-570 (June 2014): 798–801. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.798.
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It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.
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Dasgupta, Sarbani, and Banani Saha. "Study of Various Parallel Implementations of Association Rule Mining Algorithm." American Journal of Advanced Computing 1, no. 3 (2020): 1–7. http://dx.doi.org/10.15864/ajac.1305.
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In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.
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Wang, Chaoming, Anqing Fu, Weidong Li, Mingxing Li, and Tingshu Chen. "Intelligent Identification of Hidden Dangers in Hydrogen Pipeline Transmission Station Using GWO-Optimized Apriori Algorithm." Energies 17, no. 18 (2024): 4539. http://dx.doi.org/10.3390/en17184539.
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This work proposes an intelligent grey-wolf-optimizer-improved Apriori algorithm (GWO-Apriori) to mine the association rules of hidden dangers in hydrogen pipeline transmission stations. The optimal minimum support and minimum confidence are determined by GWO instead of the time-consuming trial approach. Experiments show that the average support and average confidence of association rules using GWO-Apriori increase by 29.8% and 21.3%, respectively, when compared with traditional Apriori. Overall, 59 ineffective association rules out of the total 105 rules are filtered by GWO, which dramatically improves data mining effectiveness. Moreover, 23 illogical association rules are excluded, and 12 new strong association rules ignored by the traditional Apriori are successfully mined. Compared with the inefficient and labor-intensive manual investigation, the intelligent GWO-Apriori algorithm dramatically improves pertinency and efficiency of hidden danger identification in hydrogen pipeline transmission stations.
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Prayugo, Moh Lambang, Dibyo Adi Wibowo, Moch Sjamsul Hidajat, Ery Mintorini, and Rabei Raad Ali. "Data Mining Application Analyzing Customer Purchase Patterns Using The Apriori Algorithm." Journal of Applied Intelligent System 9, no. 1 (2025): 122–35. https://doi.org/10.62411/jais.v9i1.10308.
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The study aims to implement Data Mining with Apriori Algorithm and Association Methods (shop cart analysis) to analyze the sales pattern of Kaffa Beauty Shop stores as a case study. Sales information obtained from stores is used to find out the repeated buying habits of cosmetic products. This analysis provides store owners with valuable information to make more useful decisions about product inventory management, marketing strategies, and other aspects of their business. The Apriori Algorithm implementation follows steps including data preprocessing, subsetting, frequent dataset search, and strong association rules (strong Association Rules). The results of the analysis show that there are important purchasing patterns among some cosmetic products that can be the basis of a more effective sales strategy. The study helps understand how data mining and Apriori Algorithms can be applied in business contexts such as Kaffa Beauty Shop stores. Therefore, the results of this analysis are expected to contribute greatly to improving business efficiency and optimizing marketing strategies for store owners and stakeholders. The research is also expected to show the enormous potential of data analysis to support optimal business decision making.
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Rhomadhona, Herfia, Winda Aprianti, and Jaka Permadi. "Penerapan Data Mining Terhadap Data Penjualan Prioduk Kopi Menggunakan Algoritma Apriori." Jurnal Sustainable: Jurnal Hasil Penelitian dan Industri Terapan 10, no. 2 (2021): 65–73. http://dx.doi.org/10.31629/sustainable.v10i2.3792.
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Currently, the business competition in the culinary field is very tight. Second Home is a culinary business that sells various coffee products that are in demand by the public. The large number of various coffee products causes a lot of sales transaction data to be generated by Second Home. The sales data is not managed properly, causing accumulation and useless data. The level of useless data can be minimized by using a data mining approach. The Data Mining approach aims to provide new information from coffee product sales data. Apriori algorithm is one of the algorithms of data mining that is able to find out association rules based on consumer buying patterns. Based on consumer purchasing patterns for coffee products, the owner of Second Home can provide recommendations or promotions for certain products. The dataset used in this study is 566 sales transaction data from March to May 2021. The application of data mining to sales data with the Apriori Algorithm produces 2 (two) association rules with a support value of 10% and confidence 60%. The results showed that the products purchased simultaneously were Banana fields and Es Kopi Lava Yo Lah with a confidence value of 69.43%.
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Anitha, G., R. A. Karthika, G. Bindu, and G. V. Sriramakrishnan. "Modified classic apriori algorithm for association rule mining." International Journal of Engineering & Technology 7, no. 2.21 (2018): 414. http://dx.doi.org/10.14419/ijet.v7i2.21.12455.
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In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which will create a major problem. In data mining, Association rules states about associations among the entities of known and unknown group and extracting hidden patterns in the data. Apriori algorithm is used for association rule mining. In this paper, due to limitations in rule condition, the algorithm was extended as new modified classic apriori algorithm which fulfills user stated minimum support and confidence constraints.
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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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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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Yuan, Shuli. "The Application of Information Technology for Athlete Data Analysis and Automatic Generation of Training Plans." Scalable Computing: Practice and Experience 25, no. 5 (2024): 4376–82. http://dx.doi.org/10.12694/scpe.v25i5.3136.
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In response to the demand for scientific training of sports athletes, the author combined data mining technology to study an improved sports training mode decision support evaluation system. In this regard, the author analyzed the characteristics of association rule algorithms and elaborated on their functions in data preprocessing, data mining, and pattern evaluation. Based on the software design of decision support systems, the characteristics of system operation were analyzed. At the same time, the author focused on explaining the data fusion processing of association rules in sports evaluation decision support systems, and proposed an improved Apriori algorithm output mode to improve the effectiveness of system evaluation. Compared with other algorithms such as Apriori, DC Apriori and Apriori, this algorithm has higher reliability. When the minimum confidence is increased, the advantage of prior information will gradually disappear, and the final result will be obtained. Experimental results show that this method can effectively provide support for sports training decision-making.
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Anton, Anton, and Naufal Naufal. "IMPLEMENTASI ALGORITMA APRIORI UNTUK MENENTUKAN PRODUK TERLARIS PADA TOKO I_DOCRAFT." Komputa : Jurnal Ilmiah Komputer dan Informatika 12, no. 2 (2023): 59–68. http://dx.doi.org/10.34010/komputa.v12i2.10904.
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The sales of pajama products on i_docraft have not yet leveraged data mining algorithms to analyze transactional data for optimizing sales. To avoid underperforming pajama models and determine which pajama models sell well, the utilization of the Apriori algorithm is necessary. The Apriori algorithm can discern these patterns based on transactional data. This study conducts a transactional data analysis using data mining with the Apriori algorithm. By employing this algorithm, the most frequently sold pajama products can be identified, allowing for prioritization of these models and the development of marketing strategies for other types of pajamas based on a comparison of their strengths and commonly high sales figures. The processed data yields associations rules for concurrently sold pajama items. Based on the results of the final association rules meeting both predetermined minimum support and confidence criteria, for instance, if a product with item code 7 (Cherrypie Nightdress) is purchased, then a product with item code 17 (3 in 1 Lotso Set) will likely be bought with a support value of 22.58% and a confidence value of 100%.
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Man, Mustafa Bin, Wan Aezwani Wan Abu Bakar, Zailani Abdullah, Masita@Masila Abd Jalil, and Tutut Herawan. "Mining Association Rules: A Case Study on Benchmark Dense Data." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 546. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp546-553.
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<p class="Abstract">Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.</p>
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Mardiaha, Ainul, and Yulia Yulia. "Implementasi Data Mining Menggunakan Algoritma Apriori Pada Penjualan Suku Cadang Motor." Jurnal Ilmu Komputer 14, no. 2 (2021): 125. http://dx.doi.org/10.24843/jik.2021.v14.i02.p07.
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This research was carried out to simplify or assist Candra Motor workshop owners in managing data and archives of motorcycle parts sales by applying a data mining a priori algorithm method. Data mining is an operation that uses a particular technique or method to look for different patterns or shapes in a selected data. Sales data for a year with the number of 15 items selected using the priori algorithm method. A priori algorithm is an algorithm for taking data with associative rules (association rule) to determine the associative relationship of an item combination. In a priori algorithm, it is determined frequent itemset-1, frequent itemset-2, and frequent itemset-3 so that the association rules can be obtained from previously selected data. To obtain the frequent itemset, each selected data must meet the minimum support and minimum confidence requirements. In this study using minimum support ? 7 or 0.583 and minimum confidence of 90%. So that some rules of association were obtained, where the calculation of the search for association rules manually and using WEKA software obtained the same results.By fulfilling the minimum support and minimum confidence requirements, the most sold spare parts are inner tube, Yamaha oil and MPX oil.
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Rajkamal Sarma. "Discovery of Fuzzy and Composite Fuzzy Association Rules in Meteorological Data." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 677–97. https://doi.org/10.52783/jisem.v10i37s.6505.
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Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.
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Bilqisth, Shona Chayy, and Khabib Mustofa. "Determination of Temporal Association Rules Pattern Using Apriori Algorithm." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 2 (2020): 159. http://dx.doi.org/10.22146/ijccs.51747.
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A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.
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Bahar, Uddin Mahmud, and Sharmin Afsana. "Impact analysis of harassment against women in bangladesh using association rule mining." SEU Journal of Science and Engineering 13, no. 2 (2019): 49–62. https://doi.org/10.5281/zenodo.4731451.
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In this paper, a survey-based and Apriori algorithm are used to analyze the several impacts of harassment among several age groups. Also, several factors such as frequent impacts of harassment, most vulnerable groups, women mostly facing harassment, the alleged person behind harassment, etc. are analyzed through association rule mining of the apriori algorithm and FP Growth algorithm. Then a comparison of performance between both algorithms has been shown briefly. For this analysis, data have been carefully collected from all ages.
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Jiang, Qing Chao. "The Research of Generation Algorithm of Frequent Itemsets in High-Dimensional Data." Applied Mechanics and Materials 710 (January 2015): 127–31. http://dx.doi.org/10.4028/www.scientific.net/amm.710.127.
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In the mining of association rules, the generation of frequent itemsets is a key factor that influence the efficiency and performance of the algorithm. With the increase of data dimension, it is obvious that the traditional association rules mining algorithm can’t meet the demand of high dimensional data mining. On the basis of Apriori algorithm, we put forward Split Mtrix _Apriori algorithm in this paper. By generating the Boolean matrix of the database, Split Mtrix _Apriori algorithm decreased the times of scanning database when generating the frequent itemsets. With adopting grouping processing strategy in the Boolean matrix, the algorithm can still keep high efficiency in dealing with high-dimensional data.So Split Mtrix _Apriori improved the efficiency of association rule mining significantly.
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Gao, Hui Sheng, and Ying Min Li. "An Efficient Communication Network SDH Alarm Association Rule Mining Algorithm." Advanced Materials Research 926-930 (May 2014): 1870–73. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1870.
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WINEPI algorithm is kind of data mining technology that is widely used in alarm association rules mining. Based on the classic WINEPI algorithm, we apply event window instead of time window to improve the exploration result, meanwhile we use FP-Growth algorithm framework instead of Apriori algorithm framework , thus improving efficiency. Based on the alarm time attribute we find interesting alarm association rules further. Experiments show that compared with the classic WINEPI algorithm our improved approach have advantages in reducing the mining error rate and gaining more interesting alarm association rules.