Publication
Title
Mining conjunctive sequential patterns
Author
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 nonderivability 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 inmining association rules for sequences. This approach is based on a well known combinatorial theorem: the Mbius inversion. A performance study using both synthetic and real datasets illustrates the efficiency of our mining algorithm. These new introduced patterns have a high- potential for real- life applications, especially for network monitoring and biomedical fields with the ability to get sequential association rules with all the classical statistical metrics such as confidence, conviction, lift etc.
Language
English
Source (journal)
Data mining and knowledge discovery. - Boston, Mass.
Publication
Boston, Mass. : 2008
ISSN
1384-5810
Volume/pages
17:1(2008), p. 77-93
ISI
000258290700006
Full text (Publishers DOI)
Full text (publishers version - intranet only)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 23.06.2016
Last edited 28.04.2017