Efficient Single-Pass Mining of Weighted Interesting Patterns

Authors:
Chowdhury Farhan Ahmed;Syed Khairuzzaman Tanbeer;Byeong-Soo Jeong;Young-Koo Lee
Affiliations:
Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701;Department of Computer Engineering, Kyung Hee University, Kyunggi-do, Republic of Korea 446-701
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

Mining weighted interesting patterns (WIP) [5] is an important research issue in data mining and knowledge discovery with broad applications. WIP can detect correlated patterns with a strong weight and/or support affinity. However, it still requires two database scans which are not applicable for efficient processing of the real-time data like data streams. In this paper, we propose a novel tree structure, called SPWIP-tree (Single-pass Weighted Interesting Pattern tree), that captures database information using a single-pass of database and provides efficient mining performance using a pattern growth mining approach. Extensive experimental results show that our approach outperforms the existing WIP algorithm. Moreover, it is very efficient and scalable for weighted interesting pattern mining with a single database scan.