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2007 Technical Reports

Progressive Frequent and Infrequent Patterns and Their Significant Milestones

Qian Wan and Aijun An

Technical Report CSE-2007-03

York University

May 22, 2007


A transaction database usually consists of a set of time-stamped transactions. Mining frequent patterns in transaction databases has been studied extensively in data mining research. However, most of the existing frequent pattern mining algorithms (such as Apriori and FP-growth) do not consider the time stamps associated with the transactions. In this paper, we extend the existing frequent pattern mining framework to take into account the time stamp of each transaction and discover patterns whose frequency dramatically changes over time. We define a new type of patterns, called progressive patterns, to capture the dynamic behaviour of frequent patterns in a transaction database. Progressive patterns include both progressive frequent and progressive infrequent patterns. Their frequencies increase/decrease dramatically at some time point of a transaction database. We introduce the concept of significant milestones for a progressive pattern, which are time points at which the frequency of the pattern changes most significantly. Moreover, we develop an algorithm to mine from a transaction database the complete set of progressive patterns along with their significant milestones. Our experimental studies on real-world databases illustrate that mining progressive frequent and infrequent patterns is highly promising as a practical and useful approach for discovering novel and interesting knowledge.

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