Publication
Title
MARBLES : Mining Association Rules Buried in Long Event Sequences
Author
Abstract
Sequential pattern discovery is a well-studied field in data mining. Episodes are sequential patterns that describe events that often occur in the vicinity of each other. Episodes can impose restrictions on the order of the events, which makes them a versatile technique for describing complex patterns in the sequence. Most of the research on episodes deals with special cases such as serial and parallel episodes, while discovering general episodes is surprisingly understudied. This is particularly true when it comes to discovering association rules between them. In this paper we propose an algorithm that mines association rules between two general episodes. On top of the traditional definitions of frequency and confidence, we introduce two novel confidence measures for the rules. The major challenge in mining these association rules is pattern explosion. To limit the output, we aim to eliminate all redundant rules. We define the class of closed association rules and show that this class contains all non-redundant output. To make the algorithm efficient, we use further pruning steps along the way. First of all, we generate only free and closed frequent episodes from which we create candidate rules, we speed up the evaluation of the rules, and then prune the remaining non-closed rules from the output. Finally, we provide the user with the additional option of using a confidence boost threshold to remove the less informative rules from the output.
Language
English
Source (journal)
Statistical analysis and data mining. - -
Publication
2014
ISSN
1932-1864
1932-1872
Volume/pages
7:2(2014), p. 93-110
ISI
000364190700001
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 12.04.2016
Last edited 01.08.2017
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