Discovering Partial Periodic Itemsets in Temporal Databases

Abstract

A temporal database is a collection of transactions, ordered by their timestamps. Discovering partial periodic itemsets in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered finding these itemsets in temporal databases, despite that this type of data is very common in real-life. Discovering partial periodic itemsets in temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of itemsets, and (ii) an algorithm to efficiently find all partial periodic itemsets. While a pattern-growth algorithm can be employed for the second sub-task, the first sub-task has not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to find partial periodic itemsets in temporal databases. A new measure, called periodic-frequency, has been proposed to determine the periodic interestingness of itemsets by taking into account their number of cyclic repetitions in the entire data. Moreover, the paper introduces a pattern-growth algorithm to discover all partial periodic itemsets. Experimental results demonstrate that our model is efficient.