Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
553804 | Decision Support Systems | 2010 | 11 Pages |
Abstract
In this paper a new algorithm, the Top-Down mining of Sequential patterns (TD-Seq), for mining sequential patterns from high-dimensional stock sequence databases is presented. Existing algorithms are limited by efficiency problems in dealing with high-dimensional sequence databases. To address this problem, a two-phase mining method is proposed, in which a top-down transposition-based searching strategy as well as a new support counting method are exploited. Three pruning rules were also developed to reduce the search space further. Experiments conducted on actual databases demonstrate the improved performance of TD-Seq over existing algorithms.
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Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Hongyan Liu, Fangzhou Lin, Jun He, Yunjue Cai,