Looking into the Seeds of Time: Discovering Temporal Patterns in Large Transaction Sets

Publication Type

Journal Article

Publication Date

2006

Abstract

This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent. The model is general in that (i) no constraints are placed on the interesting patterns given by the users, and (ii) two measures—inclusiveness and exclusiveness—are used to capture how well the temporal patterns match the time points given by the discovered itemsets. Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p. Using these two measures, one is able to model many temporal data mining problems appeared in the literature, as well as those that have not been studied. By exploiting the relationship within and between itemset space and pattern space simultaneously, a series of pruning techniques are developed to speed up the mining process. Experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non-temporal data mining algorithms.

Keywords

Knowledge discovery, Temporal data mining, Temporal pattern

Discipline

Information Security

Research Areas

Information Security and Trust

Publication

Information Sciences

Volume

176

Issue

8

First Page

1003

Last Page

1031

ISSN

0020-0255

Identifier

10.1016/j.ins.2005.01.019

Publisher

Elsevier

Additional URL

http://dx.doi.org/10.1016/j.ins.2005.01.019

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