SMCA: a general model for mining asynchronous periodic patterns in temporal databases

Mining periodic patterns in time series databases is an important data mining problem with many applications. Previous studies have considered synchronous periodic patterns where misaligned occurrences are not allowed. However, asynchronous periodic pattern mining has received less attention and only been discussed for a sequence of symbols where each time point contains one event. In this paper, we propose a more general model of asynchronous periodic patterns from a sequence of symbol sets where a time slot can contain multiple events. Three parameters min/spl I.bar/rep, max/spl I.bar/dis, and global/spl I.bar/rep are employed to specify the minimum number of repetitions required for a valid segment of nondisrupted pattern occurrences, the maximum allowed disturbance between two successive valid segments, and the total repetitions required for a valid sequence. A 4-phase algorithm is devised to discover periodic patterns from a time series database presented in vertical format. The experiments demonstrate good performance and scalability with large frequent patterns.

1 Introduction

Pattern mining plays an important role in data mining tasks. Various patterns have been introduced for different applications, e.g., frequent item sets [1], [6] and sequential patterns [2], [14], [18] for transaction databases, frequent episodes [10], [11], [12] in event sequences, and frequent continuities [15], [7] for intertransaction association. Periodic patterns are recurring patterns that have temporal regularities in time-series databases. Periodic patterns exist in many kinds of data. For example, tides, planet trajectories, somite formation, daily traffic patterns, and power consumptions all present certain periodic patterns. There are many emerging applications, including stock market price movement, earthquake prediction, telecommunication network fault analysis, repeat detection in DNA sequences and occurrences of recurrent illnesses, etc.