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CIKM
2009
Springer

Diverging patterns: discovering significant frequency change dissimilarities in large databases

13 years 1 months ago
Diverging patterns: discovering significant frequency change dissimilarities in large databases
In this paper, we present a framework for mining diverging patterns, a new type of contrast patterns whose frequency changes significantly differently in two data sets, e.g., it changes from a relatively low to a relatively high value in one dataset, but from high to low in the other. In this framework, a measure called diverging ratio is defined and used to discover diverging patterns. We use a four-dimensional vector to represent a pattern, and define the pattern's diverging ratio based on the angular difference between its vectors in two datasets. An algorithm is proposed to mine diverging patterns from a pair of datasets, which makes use of a standard frequent pattern mining algorithm to compute vector components efficiently. We demonstrate the effectiveness of our approach on real-world datasets, showing that the method can reveal novel knowledge from large databases. Categories and Subject Descriptors: H.2.8 [Database Management]: Database applications
Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where CIKM
Authors Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang
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