Paper Info
Title
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Abstract
Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.
Year
DOI
Venue
2009
10.1016/j.datak.2009.06.010
Data Knowl. Eng.
Keywords
DocType
Volume
hybrid temporal pattern,hybrid event sequence,interval-based pattern,point-based pattern,point-based event sequence,interval-based event sequence,previous sequential pattern mining,hybrid temporal pattern mining,event sequence,interval-based event,algorithm design,sequential pattern mining,data mining
Journal
68
Issue
ISSN
Citations 
11
0169-023X
22
PageRank 
References 
Authors
0.75
39
2
Name
Order
Citations
PageRank
Shin-Yi Wu141431.59
Yen-Liang Chen2136173.85