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.
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Ahmed, C.F., Tanbeer, S.K., Jeong, BS., Lee, YK. (2008). Efficient Single-Pass Mining of Weighted Interesting Patterns. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_41
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DOI: https://doi.org/10.1007/978-3-540-89378-3_41
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