An efficient GA-based algorithm for mining negative sequential patterns

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6118 LNAI (PART 1), pp. 262 - 273
Issue Date:
2010-12-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2009005782OK.pdf713.15 kB
Adobe PDF
Negative sequential pattern mining has attracted increasing concerns in recent datamining research because it considers negative relationships between itemsets, which are ignored by positive sequential pattern mining. However, the search space for mining negative patterns is much bigger than that for positive ones.When the support threshold is low, in particular, there will be huge amounts of negative candidates. This paper proposes a Genetic Algorithm (GA) based algorithm to find negative sequential patterns with novel crossover and mutation operations, which are efficient at passing good genes on to next generations without generating candidates. An effective dynamic fitness function and a pruning method are also provided to improve performance. The results of extensive experiments show that the proposed method can find negative patterns efficiently and has remarkable performance compared with some other algorithms of negative pattern mining. © 2010 Springer-Verlag Berlin Heidelberg.
Please use this identifier to cite or link to this item: