Yüksek Faydalı Öğe Kümeleri için Veri Madenciliği Algoritmalarının Analizi Üzerine Bir Anket

Öz

Yüksek Faydalı Öğe Seti Madenciliği (HUIM), ürünlerin satın alma miktarını ve ürün faydalarını göz önünde bulundurarak son derece önemli eğilimleri tespit etmeyi amaçlar. Statik veritabanları için ölçümlerin çoğu beklenir. Pazar sepeti incelemesi, şirket karar verme ve web yönetimi organizasyon sonuçları gibi gerçek zamanlı uygulamalarda, büyük miktarlardaki veri kümeleri, dahil edilen yeni bilgilerle yavaş yavaş gelişmektedir. Olağan madencilik hesaplamaları bu kadar karmaşık veri tabanlarını işleyemez ve faydalı verileri alamaz. Ölçülebilir bir dizi veri setinde veri toplamanın temel görevi, tamamen yüksek faydalı dizileri belirlemektir. Bulunan dizilerin sayısı yararlı olsa da her zaman son derece yüksektir. Bu makale, bir müşterinin ihtiyaçlarına daha uygun patentleri belirlemek için madde kısıtlamalarını karşılayan tekrarlanan yüksek faydalı dizi madenciliği konusunu incelemektedir. Ayrıca, bu makale yüksek değerli eleman seti madenciliğini tanıtır, modern algoritmaları, bunların uzantılarını, uygulamalarını inceler ve araştırma fırsatlarını araştırır.

Anahtar Kelimeler

• kalıp madenciliği
• öğe kümeleri
• Apriori algoritması
• ortalama kullanım

A Survey on Analysis of Data Mining Algorithms for High Utility Itemsets

Öz

High-Utility-Itemset Mining (HUIM) is meant to detect extremely important trends by considering the purchasing quantity and product benefits of items. For static databases, most of the measurements are expected. In real time applications, such as the market basket review, company decision making and web administration organization results, large quantities of datasets are slowly evolving with new knowledge incorporated. The usual mining calculations cannot handle such complex databases and retrieve useful data. The essential task of data collection in a quantifiable sequence dataset is to determine entirely high utility sequences. The number of sequences found is always extremely high, though useful. This article studies the issue of the mining of repeated high utility sequence that meet item restrictions in order to identify patents that are more suited to the needs of a customer. Also, this article introduces high-value element set mining, examines modern algorithms, their extensions, implementations, and explores research opportunities.

Anahtar Kelimeler

• pattern mining
• itemsets
• Apriori algorithm
• mean utilization

Kaynakça

1. Chu, Chun Jung, Vincent S. Tseng, and Tyne Liang. 2009. “An Efficient Algorithm for Mining High Utility Itemsets with Negative Item Values in Large Databases.” Applied Mathematics and Computation 215(2): 767–78.
2. Dam, Thu Lan, Kenli Li, Philippe Fournier-Viger, and Quang Huy Duong. 2017. “An Efficient Algorithm for Mining Top-k on-Shelf High Utility Itemsets.” Knowledge and Information Systems 52(3): 621–55.
3. Dawar, Siddharth. 2021. “Mining High-Utility Itemsets From A Transaction Database.” http://repository.iiitd.edu.in/xmlui/handle/123456789/887 (June 15, 2021).
4. Dinh, Duy Tai, Bac Le, Philippe Fournier-Viger, and Van Nam Huynh. 2018. “An Efficient Algorithm for Mining Periodic High-Utility Sequential Patterns.” Applied Intelligence 48(12): 4694–4714.
5. Dong, Xiangjun et al. 2019. “Mining Top- k Useful Negative Sequential Patterns via Learning.” IEEE transactions on neural networks and learning systems 30(9): 2764–78.
6. Duan, Yiheng et al. 2015. “Detective: Automatically Identify and Analyze Malware Processes in Forensic Scenarios via DLLs.” In IEEE International Conference on Communications, Institute of Electrical and Electronics Engineers Inc., 5691–96.
7. Duong, Hai, Tin Truong, Anh Tran, and Bac Le. 2020. “Fast Generation of Sequential Patterns with Item Constraints from Concise Representations.” Knowledge and Information Systems 62(6): 2191–2223.
8. Duong, Quang Huy et al. 2018. “Efficient High Utility Itemset Mining Using Buffered Utility-Lists.” Applied Intelligence 48(7): 1859–77. Fournier-Viger, Philippe et al. 2021. “Discovering Periodic High Utility Itemsets in a Discrete Sequence.” In Periodic Pattern Mining: Theory, Algorithms and Applications,.

9. Fournier-Viger, Philippe, Jerry Chun-Wei Lin, Tin Truong-Chi, and Roger Nkambou. 2019. “A Survey of High Utility Itemset Mining.” In High-Utility Pattern Mining, , 1–45.
10. Gan, Wensheng et al. 2018. “A Survey of Incremental High-Utility Itemset Mining.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(2).
11. Han, Xixian, Xianmin Liu, Jianzhong Li, and Hong Gao. 2021. “Efficient Top-k High Utility Itemset Mining on Massive Data.” Information Sciences 557: 382–406.
12. Kenny Kumar, Mathe John, and Dipti Rana. 2021. “High Average Utility Itemset Mining: A Survey.” In Lecture Notes on Data Engineering and Communications Technologies, Springer Science and Business Media Deutschland GmbH, 347–74.
13. Leleu, Marion, Christophe Rigotti, Jean-François Boulicaut, and Guillaume Euvrard. 2003. “Constraint-Based Mining of Sequential Patterns over Datasets with Consecutive Repetitions.” In PKDD 2003: Knowledge Discovery in Databases: PKDD 2003 , , 303–14.
14. Liao, Jiyong, Sheng Wu, and Ailian Liu. 2021. “High Utility Itemsets Mining Based on Divide-and-Conquer Strategy.” Wireless Personal Communications 116(3): 1639–57.
15. Logeswaran, K et al. 2021. “A Survey on Metaheuristic Nature Inspired Computations Used for Mining of Association Rule, Frequent Itemset and High Utility Itemset.” IOP Conference Series: Materials Science and Engineering 1055(1): 012103.
16. Masseglia, F., P. Poncelet, and M. Teisseire. 2009. “Efficient Mining of Sequential Patterns with Time Constraints: Reducing the Combinations.” Expert Systems with Applications 36(2 PART 2): 2677–90.
17. Ming-Yen Linand, Sue-Chen Hsueh, and Tzer-Fu Tu. 2019. “Mining High-Utility Itemsets of Generalized Quantity with Pattern-Growth Structures.” In Proceedings of the 2nd Sensor Networks and Signal Processing (SNSP 2019), 19–22 November 2019, Hualien, Taiwan, http://www.springer.com/series/8767.
18. Niu, Kun et al. 2017. “A Developed Apriori Algorithm Based on Frequent Matrix.” In ACM International Conference Proceeding Series, Association for Computing Machinery, 55–58.
19. Nouioua, Mourad et al. 2021. “FHUQI-Miner: Fast High Utility Quantitative Itemset Mining.” Applied Intelligence. Pazhaniraja, N., and S. Sountharrajan. 2020. “High Utility Itemset Mining Using Dolphin Echolocation Optimization.” Journal of Ambient Intelligence and Humanized Computing.
20. Saqib Nawaz, M et al. 2021. “Investigating Crossover Operators in Genetic Algorithms for High-Utility Itemset Mining.” In ACIIDS 2021: Intelligent Information and Database Systems , , 16–28.
21. Saqib Nawaz, M, Philippe Fournier-Viger, and Unil Yun. 2021. “Mining High Utility Itemsets with Hill Climbing and Simulated Annealing.” ACM Transactions of Management Information Systems. https://doi.org/XXXXX.
22. Srilatha, G., and N Subhash Chandra. 2021. “Robust Frequency Affinity-Based High Utility Itemset Mining Approach Using Multiple Minimum Utility.” Materials Today: Proceedings.
23. Truong, Tin et al. 2021. “Efficient Algorithms for Mining Frequent High Utility Sequences with Constraints.” Information Sciences 568: 239–64.
24. Tzung-Pei Hong, Cho-Han Lee, and Shyue-Liang Wang. 2009. “Mining High Average-Utility Itemsets.” In Proceedings 2009 International Conference on Systems, Man and Cybernetics : October 11-14, 2009 : San Antonio, Texas, USA, IEEE.
25. Vivekanandan, S J, S P Ammu, R Sripriyadharshini, and T R Preetha. 2021. “Computation Of High Utility Item Sets By Using Range Of Utility Technique.” Journal of University of Shanghai for Science and Technology.
26. Yang, Qiang. 2012. “Mining Top-K High Utility Itemsets.” In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., ACM, 1580.