DISCOVERING IMPORTANT SEQUENTIAL PATTERNS WITH LENGTH-DECREASING WEIGHTED SUPPORT CONSTRAINTS

Unil Yun () and Keun Ho Ryu ()
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Unil Yun: Division of Computer Engineering, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju, Chungbuk, South Korea
Keun Ho Ryu: Division of Computer Engineering, College of Electrical & Computer Engineering, Chungbuk National University, Cheongju, Chungbuk, South Korea

International Journal of Information Technology & Decision Making (IJITDM), 2010, vol. 09, issue 04, 575-599

Abstract: Sequential pattern mining with constraints has been developed to improve the efficiency and effectiveness in mining process. Specifically, there are two interesting constraints for sequential pattern mining. First, some sequences are more important and others are less important. Weight constraints consider the importance of sequences and items within sequences. Second, patterns including only a few items are interesting if they have high support. Meanwhile, long patterns can be interesting although their supports are relatively small. Weight constraints and length-decreasing support constraints are two paradigms aimed at finding important sequential patterns and reducing uninteresting patterns. Although weight and length-decreasing support constraints are vital elements, it is hard to consider both constraints by using previous approaches. In this paper, we integrate weight and length-decreasing support constraints by pushing two constraints into the prefix projection growth method. For pruning techniques, we define the Weighted Smallest Valid Extension property and apply the property to our pruning methods for reducing search space. In performance test, we show that our algorithm mines important sequential patterns with length-decreasing support constraints.

Keywords: Data mining; knowledge discovery; weighted sequential pattern mining; length-decreasing support constraints (search for similar items in EconPapers)
Date: 2010
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DOI: 10.1142/S0219622010003968

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