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An efficient approach for finding weighted sequential patterns from sequence databases
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AI
DOI：10.1007/s10489-014-0530-4.png)
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Abstract
En 中文
Weighted sequential pattern mining has recently been discussed in the field of data mining. Different from traditional sequential pattern mining, this kind of mining considers different significances of items in real applications, such as cost or profit. Most of the related studies adopt the maximum weighted upper-bound model to find weighted sequential patterns, but they generate a large number of unpromising candidate subsequences. In this study, we thus propose an efficient approach for finding weighted sequential patterns from sequence databases. In particular, a tightening strategy in the proposed approach is proposed to obtain more accurate weighted upper-bounds for subsequences in mining. Through the experimental evaluation, the results also show the proposed approach has good performance in terms of pruning effectiveness and execution efficiency.
Keywords:
Data mining
Sequential pattern
Weighted sequential pattern
Weighted frequent patterns
Upper bound
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3.5
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APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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