Relevant bibliographies by topics / FP growth algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'FP growth algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "FP growth algorithm"

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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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Dissertations / Theses on the topic "FP growth algorithm"

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Ivaškevičius, Klaidas. "Daugiamačių sekų šablonų analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2012~D_20140630_173416-93518.

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Pagrindinis šio magistro baigiamojo darbo tikslas buvo apžvelgti kai kurių algoritmų ir jų kombinacijų pritaikymą daugiamačiams sekų šablonams analizuoti ir įgyvendinti algoritmą, gebantį tai atlikti. Buvo aprašyta FP-Tree medžio struktūra, kuri yra skirta kompaktiškai saugoti kritiniams (pvz., dažnai pasikartojantiems) duomenims, pateiktas FP-Growth algoritmas, galintis analizuoti tokią duomenų struktūrą ir rezultate pateikiantis visų dažnų elementų šablonų aibę. Pristatyta modifikuotų FP-Growth ir PrefixSpan algoritmų kombinacija – MD-PS-FPG algoritmas, pateikti kai kurių atliktų testavimų rezultatai, tolimesnių darbų pagrindiniai tikslai ir pan.<br>The main goal of this master final work was to present some of the algorithms and their combinations for the multidimensional sequence pattern mining and implement an algorithm, that is capable of doing that. FP-Tree, that is used to store critical (for example, often repeated) data, was described. FP-Growth algorithm, that can analyze FP-Tree structure and give frequent pattern set as a result, was presented. MD-PS-FPG algorithm – a combination of modified FP-Growth and PrefixSpan algorithms – was introduced. The results of some tests, further work objectives and other things were also presented.
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Giavoli, Andrea. "Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Adattamento di un algoritmo di apprendimento automatico per la caratterizzazione e la ricerca di frequent patterns su macchine automatiche." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9055/.

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La tesi da me svolta durante questi ultimi sei mesi è stata sviluppata presso i laboratori di ricerca di IMA S.p.a.. IMA (Industria Macchine Automatiche) è una azienda italiana che naque nel 1961 a Bologna ed oggi riveste il ruolo di leader mondiale nella produzione di macchine automatiche per il packaging di medicinali. Vorrei subito mettere in luce che in tale contesto applicativo l’utilizzo di algoritmi di data-mining risulta essere ostico a causa dei due ambienti in cui mi trovo. Il primo è quello delle macchine automatiche che operano con sistemi in tempo reale dato che non presentano a pieno le risorse di cui necessitano tali algoritmi. Il secondo è relativo alla produzione di farmaci in quanto vige una normativa internazionale molto restrittiva che impone il tracciamento di tutti gli eventi trascorsi durante l’impacchettamento ma che non permette la visione al mondo esterno di questi dati sensibili. Emerge immediatamente l’interesse nell’utilizzo di tali informazioni che potrebbero far affiorare degli eventi riconducibili a un problema della macchina o a un qualche tipo di errore al fine di migliorare l’efficacia e l’efficienza dei prodotti IMA. Lo sforzo maggiore per riuscire ad ideare una strategia applicativa è stata nella comprensione ed interpretazione dei messaggi relativi agli aspetti software. Essendo i dati molti, chiusi, e le macchine con scarse risorse per poter applicare a dovere gli algoritmi di data mining ho provveduto ad adottare diversi approcci in diversi contesti applicativi: • Sistema di identificazione automatica di errore al fine di aumentare di diminuire i tempi di correzione di essi. • Modifica di un algoritmo di letteratura per la caratterizzazione della macchina. La trattazione è così strutturata: • Capitolo 1: descrive la macchina automatica IMA Adapta della quale ci sono stati forniti i vari file di log. Essendo lei l’oggetto di analisi per questo lavoro verranno anche riportati quali sono i flussi di informazioni che essa genera. • Capitolo 2: verranno riportati degli screenshoot dei dati in mio possesso al fine di, tramite un’analisi esplorativa, interpretarli e produrre una formulazione di idee/proposte applicabili agli algoritmi di Machine Learning noti in letteratura. • Capitolo 3 (identificazione di errore): in questo capitolo vengono riportati i contesti applicativi da me progettati al fine di implementare una infrastruttura che possa soddisfare il requisito, titolo di questo capitolo. • Capitolo 4 (caratterizzazione della macchina): definirò l’algoritmo utilizzato, FP-Growth, e mostrerò le modifiche effettuate al fine di poterlo impiegare all’interno di macchine automatiche rispettando i limiti stringenti di: tempo di cpu, memoria, operazioni di I/O e soprattutto la non possibilità di aver a disposizione l’intero dataset ma solamente delle sottoporzioni. Inoltre verranno generati dei DataSet per il testing di dell’algoritmo FP-Growth modificato.
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Giudice, Riccardo. "Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time: Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/9054/.

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Analisi e applicazione dei processi di data mining al flusso informativo di sistemi real-time. Implementazione e analisi di un algoritmo autoadattivo per la ricerca di frequent patterns su macchine automatiche.
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Schlegel, Benjamin. "Frequent itemset mining on multiprocessor systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-141763.

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Frequent itemset mining is an important building block in many data mining applications like market basket analysis, recommendation, web-mining, fraud detection, and gene expression analysis. In many of them, the datasets being mined can easily grow up to hundreds of gigabytes or even terabytes of data. Hence, efficient algorithms are required to process such large amounts of data. In recent years, there have been many frequent-itemset mining algorithms proposed, which however (1) often have high memory requirements and (2) do not exploit the large degrees of parallelism provided by modern multiprocessor systems. The high memory requirements arise mainly from inefficient data structures that have only been shown to be sufficient for small datasets. For large datasets, however, the use of these data structures force the algorithms to go out-of-core, i.e., they have to access secondary memory, which leads to serious performance degradations. Exploiting available parallelism is further required to mine large datasets because the serial performance of processors almost stopped increasing. Algorithms should therefore exploit the large number of available threads and also the other kinds of parallelism (e.g., vector instruction sets) besides thread-level parallelism. In this work, we tackle the high memory requirements of frequent itemset mining twofold: we (1) compress the datasets being mined because they must be kept in main memory during several mining invocations and (2) improve existing mining algorithms with memory-efficient data structures. For compressing the datasets, we employ efficient encodings that show a good compression performance on a wide variety of realistic datasets, i.e., the size of the datasets is reduced by up to 6.4x. The encodings can further be applied directly while loading the dataset from disk or network. Since encoding and decoding is repeatedly required for loading and mining the datasets, we reduce its costs by providing parallel encodings that achieve high throughputs for both tasks. For a memory-efficient representation of the mining algorithms’ intermediate data, we propose compact data structures and even employ explicit compression. Both methods together reduce the intermediate data’s size by up to 25x. The smaller memory requirements avoid or delay expensive out-of-core computation when large datasets are mined. For coping with the high parallelism provided by current multiprocessor systems, we identify the performance hot spots and scalability issues of existing frequent-itemset mining algorithms. The hot spots, which form basic building blocks of these algorithms, cover (1) counting the frequency of fixed-length strings, (2) building prefix trees, (3) compressing integer values, and (4) intersecting lists of sorted integer values or bitmaps. For all of them, we discuss how to exploit available parallelism and provide scalable solutions. Furthermore, almost all components of the mining algorithms must be parallelized to keep the sequential fraction of the algorithms as small as possible. We integrate the parallelized building blocks and components into three well-known mining algorithms and further analyze the impact of certain existing optimizations. Our algorithms are already single-threaded often up an order of magnitude faster than existing highly optimized algorithms and further scale almost linear on a large 32-core multiprocessor system. Although our optimizations are intended for frequent-itemset mining algorithms, they can be applied with only minor changes to algorithms that are used for mining of other types of itemsets.
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Huang, Kai-Chun, and 黃楷鈞. "An Implementation of the parallelized FP-Growth Algorithm in the Hadoop Yarn Platform." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/86500170426808682472.

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碩士<br>國立臺灣科技大學<br>資訊管理系<br>104<br>Due to rapid advance of the Internet, data have been generated in an extremely fast speed. In an era of data explosion, people are eager to extract valuable information from large amount of data. Hadoop is a very popular framework for data mining on big datasets. In a Hadoop platform, one can splits a computing task into a set of subtasks to be processed by different computing nodes with the Map and Reduce operations. Furthermore, the Hadoop distributed file system, HDFS, is able to split a big dataset into file segments and to allocate them among different computing nodes of the platform. Therefore, Hadoop computing platform is well suited for data mining on big datasets. The FP-Growth is a famous frequent itemset mining algorithm with many realife applications. However, the traditional FP-Growth algorithm can only deal with small datasets. It is inadequate in handling big datasets. In thesis, we implemented a parallel FP-Growth algorithm in the new Hadoop Yarn platform. The experimental results showed that the new platform offered better performance on the FP-Growth algorithm than an older Hadoop platform did on many different datasets.
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Huang, Jheng-Nan, and 黃正男. "Improving the Performance of the FP-Growth Mining Algorithm in Very Large Databases." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/cgac8y.

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碩士<br>國立高雄大學<br>資訊工程學系碩士班<br>97<br>Along with the progress of information techniques and the increase of information need, some databases in the real world grow very quickly and their sizes become very huge. If the FP-Growth procedure is directly executed on these databases to mine association rules, the computer memory may not allow all nodes of a FP-tree generated from a huge database. This means the FP-Growth procedure will be inefficient because of the high page fault rate in the mining process. The thesis thus focuses on solving or easing off the mining problems incurred from memory limitation. A sophisticated mining approach with a flexible partition of items is proposed to effectively derive association rules under the constraint of memory limitation. The proposed approach can be divided into three phases. In the first phase, the domain items that appear in a transaction database are divided into a set of groups under the constraint that the number of items in each group cannot exceed a threshold. The groups in the partition may thus be independent or dependent according to the given data. In the second phase, we slightly modify the FP-tree structure by keeping III the transaction numbers for each branch to effectively handle the cross-group mining problem. A modified FP tree is first built from each group of items and the FP-Growth procedure mine the frequent itemsets in individual groups. A compact representation for the frequent itemsets from each group is then designed to save the storage of itemsets. In the third phase, the frequent itemsets in the groups are then merged with the aid of the encoded representation. The proposed approach can make the partition flexible and balanced, thus causing the mining process under the memory limitation always feasible.
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Hung, Chen-Ju, and 洪辰儒. "A CCAS Scheduling for FP-Growth Algorithm in an Inter-Cloud Computing Environment." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/wqr99s.

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碩士<br>國立彰化師範大學<br>資訊工程學系<br>105<br>At present enterprises need to calculate more and more data in environment, single cloud system have limited computing power and unable to process a huge data. In this time coordinate the appropriate cloud to compute so that the overall operation becomes efficiency, inter-cloud system generated based on this concept. Moreover, FP-Growth algorithm is a famous tool in the field of Data Mining for finding association rules from data. Thus the FP-Growth algorithm running on the inter-cloud environment based on a scheduling algorithm is presented. Currently FP-Growth algorithm mostly process in single cloud, processing in inter-cloud system is still emerging field. When several cloud systems are interconnected, data need be processed over various cloud systems with flexible and/or efficient methods. In this paper, a scheduling algorithm based on the Compute Capacity Aware Scheduling (CCAS) method is proposed. If the data volume does not exceed the maximal compute capacity of default cloud, only this single cloud will be used to perform the computation; otherwise, heterogeneous clusters organized as inter-cloud system will coordinate the computation. The experimental results show that the proposed method can achieve more efficient computing in inter-cloud environment.
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Tang, Jimmy Hau-Chieh, and 湯皓傑. "The Improvement of FP-Growth Algorithm using Data Condensing & Task-Independent Parallelization Techniques." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/87375127637595942310.

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碩士<br>元智大學<br>資訊管理研究所<br>92<br>We proposed a data condensing algorithm of association rule mining (ARM) to solve the problem of the FP-Growth algorithm which gets the poor performance when the available physical memory capacity is less than required for working. By using the condensing algorithm and the FP-Subtree algorithm that inspired from the “partition-based projection” concept, we could reduce the working memory size of FP-Growth and I/O costs of ARM. In addition, we also developed a task-independent parallelized ARM system architecture, which could reduce the communicating costs during the entire process and makes the workstations in the system work more efficiently.
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Wang, Nien-Hung, and 王年宏. "Using FP-growth Genetic Algorithm to Construct Neural-Fuzzy Control Systems Based on Reinforcement Learning Scheme." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/68499373612441324994.

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碩士<br>國立交通大學<br>電機與控制工程系所<br>96<br>Recently, evolutionary algorithms are widely applied in several regions. In the traditionalgenetic algorithm, the chromosome is evolved by using random search to execute crossover and mutation. However, the gene with good performance may be tuned repeatedly and the evolutionary time will be much longer. In this thesis, a genetic algorithm based on data mining is adopted to solve this problem. By using the ability of looking for association data, the gene point associating with the improvement of fitness value can be found. Hence, suitable crossover points and mutation points can be found systematically, and the algorithm can be converged more efficiently. In this thesis, a neural-fuzzy control system using reinforcement learning is constructed. This thesis also uses the technique of data mining to enhance the evolution efficiency of the algorithm. The asymmetric crossover and mutation is proposed to learn the parameters and structure of neural-fuzzy control system. The ball and beam control system and chaos control system are used as examples to test learning ability and controllability of the proposed system. The experimental results show the system is satisfactory.
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TAO, HSIN-CHEN, and 陶信辰. "Utilizing Genetic Algorithm and Fuzzy FP-Growth to Mine Fuzzy Association Rules－Using Yunlin and Chiayi Air Quality Monitoring Station as An Example." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/v7tnk8.

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碩士<br>國立雲林科技大學<br>工業工程與管理系<br>104<br>Global air quality deteriorating in recent years, research indicates whether longterm or short-term in a high concentration of fine suspended particles environment will increase the risk of respiratory disease and suffering from pneumonia. Past air pollution related studies mostly used statistical verification techniques to explore meteorological factors, air pollution factor and suspended particulates (PM10) of relevance. The EPA set the fine suspended particles (PM2.5) indicator in 2014. In this study, in order to explore association rules among the meteorological factors & air pollution & aerosols factor (PM2.5, PM10), research data was chosen the monitoring data from 2012 to 2014 Yunlin and Chiayi air quality monitoring station. Applied the association rule to mine air pollution rules that may analyze the reasons of air pollution problems. Study on the association rules related to the need for self-given minimum support (Minimum Support) and the minimum reliability (Minimum Confidence) threshold screening as the rule of the (Threshold). In the case of increasing the amount of data to develop its own threshold will make the results more and more unstable. In this study, another indicator of association rules: gain value (Lift), when used as the threshold required to consider setting target variable and proposes genetic algorithm for solving the largest overall gain of fuzzy threshold parameter value (Total Fuzzy Lift) under , as the optimal threshold of association rules research methods and processes. Utilization of Fuzzy FP-Growth identify air pollutants (PM2.5, PM10) for the target variables of association rules, found that use of optimized mining association rules threshold rule out there will be a large variation in the average value is smaller overall Fuzzy gain value (Total Fuzzy Lift), compared with users to develop their own rules for screening threshold for the more meaningful association rules.
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Book chapters on the topic "FP growth algorithm"

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Jia, Kuikui, and Haibin Liu. "An Improved FP-Growth Algorithm Based on SOM Partition." In Communications in Computer and Information Science. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6385-5_15.

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Nurlybayeva, Sabina, Iskander Akhmetov, Alexander Gelbukh, and Rustam Mussabayev. "Plagiarism Detection in Students’ Answers Using FP-Growth Algorithm." In Advances in Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89820-5_12.

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Wang, Yanhui, Shujun Wang, and Shuai Lin. "Correlation Failure Analysis Based on the Improved FP-Growth Algorithm." In Proceedings of the 2015 International Conference on Electrical and Information Technologies for Rail Transportation. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49370-0_14.

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Zhang, Jianlin, Suozhu Wang, Huiying Lv, and Chaoliang Zhou. "Research on Application of FP-growth Algorithm for Lottery Analysis." In LISS 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_184.

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Gruca, Aleksandra. "Improvement of FP-Growth Algorithm for Mining Description-Oriented Rules." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02309-0_19.

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Chen, Ke, Lijun Zhang, Sansi Li, and Wende Ke. "Research on Association Rules Parallel Algorithm Based on FP-Growth." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27452-7_33.

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Shang, Yingmei. "Application and Research of FP Growth Algorithm in Data Mining." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-05484-6_140.

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Ye, Qianhao, Wenhui Lu, and Shiyong Ning. "Improved Algorithm of FP-Growth Based on Strong Data Correlation." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3980-6_3.

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Yuan, Jingbo, and Shunli Ding. "Research and Improvement on Association Rule Algorithm Based on FP-Growth." In Web Information Systems and Mining. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_41.

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Wang, Huaibin, Yuanchao Liu, and Chundong Wang. "Research on Association Rule Algorithm Based on Distributed and Weighted FP-Growth." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25989-0_24.

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Conference papers on the topic "FP growth algorithm"

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Bao, Chun, Yang Tang, Bingheng Yang, et al. "Mining analysis of traffic accident features based on Fp-growth algorithm and Apriori algorithm." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3056036.

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MohanaPriya, D., P. Anu, N. Kalyani, S. Rekha, R. Praba, and S. Sasikala. "Safeguarding Privacy for Cloud Computing's Encrypted Data Using Association Rule Mining Algorithm and FP Growth Algorithm." In 2024 3rd Edition of IEEE Delhi Section Flagship Conference (DELCON). IEEE, 2024. https://doi.org/10.1109/delcon64804.2024.10866727.

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Yang, Tao, Kun Zhang, Chao Cong, Lingji Kong, and Dengkao Xi. "Network Attack Detection Method of Power Equipment Communication System based on FP-Growth Algorithm." In 2024 International Conference on Artificial Intelligence and Power Systems (AIPS). IEEE, 2024. http://dx.doi.org/10.1109/aips64124.2024.00106.

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Li, Jiangsheng, Mengxi Zhang, Guangxiang Jin, Qi Wei, and Min Liu. "Defect correlation analysis of full-level collaborative power communication network based on FP-Growth algorithm." In 6th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2024), edited by Dehai Zhang and Tao Lei. SPIE, 2024. http://dx.doi.org/10.1117/12.3037444.

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Huang, Mei, and Rui Hu. "Design of a College Student Employment Guidance Platform Based on Spark Framework and FP Growth Algorithm." In 2024 8th International Symposium on Computer Science and Intelligent Control (ISCSIC). IEEE, 2024. https://doi.org/10.1109/iscsic64297.2024.00027.

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Chen, Min, XueDong Gao, and HuiFei Li. "An efficient parallel FP-Growth algorithm." In 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2009. http://dx.doi.org/10.1109/cyberc.2009.5342148.

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Borgelt, Christian. "An implementation of the FP-growth algorithm." In the 1st international workshop. ACM Press, 2005. http://dx.doi.org/10.1145/1133905.1133907.

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Shi, Xiujin, Shaozong Chen, and Hui Yang. "DFPS: Distributed FP-growth algorithm based on Spark." In 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2017. http://dx.doi.org/10.1109/iaeac.2017.8054308.

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Anggrainingsih, Rini, Nach Rowi Khoirudin, and Haryono Setiadi. "Discovering drugs combination pattern using FP-growth algorithm." In 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI). IEEE, 2017. http://dx.doi.org/10.1109/eecsi.2017.8239203.

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Jiang, Hao, and He Meng. "A Parallel FP-Growth Algorithm Based on GPU." In 2017 IEEE 14th International Conference on e-Business Engineering (ICEBE). IEEE, 2017. http://dx.doi.org/10.1109/icebe.2017.24.

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