Authors:
Oscar Quiroga
;
Joaquim Meléndez
and
Sergio Herraiz
Affiliation:
University of Girona, Spain
Keyword(s):
Data Mining, Event Sequences, Frequent Episodes, Pattern Discovery.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Concept Mining
;
Context Discovery
;
Data Analytics
;
Data Engineering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Process Mining
;
Symbolic Systems
Abstract:
Pattern discovery in event sequences is based on the mining of frequent episodes. Patterns are the result of the assessment of frequent episodes using episode rules. However, with a simple search usually a huge number of frequent episodes and rules are found, then, methods to recognise the most significant patterns and to properly measure the frequency of the episodes, are required. In this paper, two new indexes called cohesion and backward-confidence of the episodes are proposed to help in the extraction of significant patterns. Also, two methods to find the maximal number of non-redundant occurrences of serial and parallel episodes are presented. Experimental results demonstrate the compactness of the mining result and the efficiency of our mining algorithms.