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

• Type: Journal
• Year: 2009
• Where: CIKM
• Authors: Aijun An, Qian Wan, Jiashu Zhao, Xiangji Huang
• Keywords: CIKM 2009, Database, Information Technology, Mining, Standard Frequent Pattern