Discovering Partial Periodic Patterns in Discrete Data Sequences

Abstract

The problem of partial periodic pattern mining in a discrete data sequence is to find subsequences that appear periodically and frequently in the data sequence. Two essential subproblems are the efficient mining of frequent patterns and the automatic discovery of periods that correspond to these patterns. Previous methods for this problem in event sequence databases assume that the periods are given in advance or require additional database scans to compute periods that define candidate patterns. In this work, we propose a new structure, the abbreviated list table (ALT), and several efficient algorithms to compute the periods and the patterns, that require only a small number of passes. A performance study is presented to demonstrate the effectiveness and efficiency of our method.