Mining conjunctive sequential patterns

Abstract. In this paper we aim at extending the non-derivable condensed representation in frequent itemset mining to sequential pattern mining. We start by showing a negative example: in the context of frequent sequences, the notion of non-derivability is meaningless. Therefore, we extend our focus to the mining of conjunctions of sequences. Besides of being of practical importance, this class of patterns has some nice theoretical properties. Based on a new unexploited theoretical definition of equivalence classes for sequential patterns, we are able to extend the notion of a non-derivable itemset to the sequence domain. We present a new depth-first approach to mine non-derivable conjunctive sequential patterns and show its use in mining association rules for sequences. This approach is based on a well known combinatorial theorem: the M

• Type: Journal
• Year: 2008
• Where: DATAMINE
• Authors: Chedy Raïssi, Toon Calders, Pascal Poncelet
• Keywords: DATAMINE 2008, Mining, Sequential Pattern Mining, Sequential Patterns