Interactive mining of high utility patterns over data streams |
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Authors: | Chowdhury Farhan Ahmed Syed Khairuzzaman Tanbeer Byeong-Soo Jeong Ho-Jin Choi |
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Affiliation: | 1. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, 518055, China;2. School of Computer Sciences and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China;3. Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences (HVL), Bergen 5020, Norway;4. Iwate Prefectural University, Morioka 020-8550, Japan;5. Department of Computer Sciences, University of Auckland, Auckland 303476, New Zealand;1. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea;2. Faculty of Software and Information Science, Iwate Prefectural University (IPU), Iwate, Japan |
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Abstract: | High utility pattern (HUP) mining over data streams has become a challenging research issue in data mining. When a data stream flows through, the old information may not be interesting in the current time period. Therefore, incremental HUP mining is necessary over data streams. Even though some methods have been proposed to discover recent HUPs by using a sliding window, they suffer from the level-wise candidate generation-and-test problem. Hence, they need a large amount of execution time and memory. Moreover, their data structures are not suitable for interactive mining. To solve these problems of the existing algorithms, in this paper, we propose a novel tree structure, called HUS-tree (high utility stream tree) and a new algorithm, called HUPMS (high utility pattern mining over stream data) for incremental and interactive HUP mining over data streams with a sliding window. By capturing the important information of stream data into an HUS-tree, our HUPMS algorithm can mine all the HUPs in the current window with a pattern growth approach. Furthermore, HUS-tree is very efficient for interactive mining. Extensive performance analyses show that our algorithm is very efficient for incremental and interactive HUP mining over data streams and significantly outperforms the existing sliding window-based HUP mining algorithms. |
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