Finding calendar-based periodic patterns

Abstract

Mining patterns in a market-basket dataset is a well-stated problem. There are a number of approaches to deal with this problem. Different types of patterns may be present in a dataset. An interesting one is patterns that hold seasonally, which are called calendar-based patterns. Earlier methods require periods to be specified by the user. We present here a method which is able to extract different types of periodic patterns that may exist in a temporal market-basket dataset and it is not needed for the user to specify the periods in advance. We consider the time-stamps as a hierarchical data structure and then extract different types of patterns. The algorithm can detect both wholly and partially periodic patterns. Although we have applied our approach to market-basket dataset, the approach can be used for any event related dataset where the events are associated with time intervals.