Mining disjunctive sequential patterns from news stream

Authors:
Kazuhiro Shimizu;Isamu Shioya;Takao Miura
Affiliations:
Dept. of Elect. & Elect. Engr., HOSEI University, Tokyo, Japan;Dept. of Management and Informatics, SANNO University, Isehara, Kanagawa, Japan;Dept. of Elect. & Elect. Engr., HOSEI University, Tokyo, Japan
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

Frequent disjunctive pattern is known to be a sophisticated method of text mining in a single document that satisfies antimonotonicity, by which we can discuss efficient algorithm based on APRIORI. In this work, we propose a new online and single-pass algorithm by which we can extract current frequent disjunctive patterns by a weighting method for past events from a news stream. And we discuss some experimental results.