摘要: High-utility itemset mining is an emerging research area in the field of Data Mining. Several algorithms were proposed to find high-utility itemsets from transaction databases and use a data structure called UP-tree for their working. However, based on generate lot candidates due limited information availability computing utility value estimates itemsets. In this paper, we present named UP-Hist tree which maintains histogram item quantities with each node tree. The allows computation better effective pruning search space. Extensive experiments real as well synthetic datasets show that our algorithm outperforms state art pattern-growth terms total number candidate high generated needs be verified.
acm.org
sci-hub.se 参考文章(18)
Ramakrishnan Srikant, Rakesh Agrawal, Fast algorithms for mining association rules very large data bases. pp. 580- 592 ,(1998)
Ramakrishnan Srikant, Rakesh Agrawal, Fast Algorithms for Mining Association Rules in Large Databases very large data bases. pp. 487- 499 ,(1994)
Bai-En Shie, Hui-Fang Hsiao, Vincent S. Tseng, Philip S. Yu, Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments Database Systems for Advanced Applications. pp. 224- 238 ,(2011) , 10.1007/978-3-642-20149-3_18
Junfu Yin, Zhigang Zheng, Longbing Cao, Yin Song, Wei Wei, Efficiently Mining Top-K High Utility Sequential Patterns international conference on data mining. pp. 1259- 1264 ,(2013) , 10.1109/ICDM.2013.148
Carson Kai-Sang Leung, Quamrul I. Khan, Zhan Li, Tariqul Hoque, CanTree: a canonical-order tree for incremental frequent-pattern mining Knowledge and Information Systems. ,vol. 11, pp. 287- 311 ,(2007) , 10.1007/S10115-006-0032-8
Vincent S. Tseng, Bai-En Shie, Cheng-Wei Wu, Philip S. Yu, Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases IEEE Transactions on Knowledge and Data Engineering. ,vol. 25, pp. 1772- 1786 ,(2013) , 10.1109/TKDE.2012.59
Jiawei Han, Jian Pei, Yiwen Yin, Mining frequent patterns without candidate generation international conference on management of data. ,vol. 29, pp. 1- 12 ,(2000) , 10.1145/335191.335372
Tianjun Lu, Yang Liu, Le Wang, An Algorithm of Top-k High Utility Itemsets Mining over Data Stream Journal of Software. ,vol. 9, pp. 2342- 2347 ,(2014) , 10.4304/JSW.9.9.2342-2347
Philippe Fournier-Viger, Cheng-Wei Wu, Souleymane Zida, Vincent S. Tseng, FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning Lecture Notes in Computer Science. pp. 83- 92 ,(2014) , 10.1007/978-3-319-08326-1_9
Hua-Fu Li, Hsin-Yun Huang, Yi-Cheng Chen, Yu-Jiun Liu, Suh-Yin Lee, Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams international conference on data mining. pp. 881- 886 ,(2008) , 10.1109/ICDM.2008.107