Diverging patterns: discovering significant frequency change dissimilarities in large databases

Authors:
Aijun An;Qian Wan;Jiashu Zhao;Xiangji Huang
Affiliations:
York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

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.