Paper Info
Title
Comparative analysis of sequence weighting approaches for mining time-interval weighted sequential patterns
Abstract
Unlike the general sequential pattern mining that considers only the generation order of data elements, mining weighted sequential patterns aims to get more interesting sequential patterns by considering the weights of data elements in a target sequence database in addition to their generation order. In general, for a sequence or a sequential pattern, not only the generation order of data elements but also their generation times and time-intervals are important because they can be helpful in finding more interesting sequential patterns. Applying the mining method of time-interval weighted sequential (TiWS) patterns that has been proposed in our previous work, this paper proposes several sequence weighting approaches to get the time-interval weight of a sequence in mining TiWS patterns for a sequence database, and the effectiveness of each approach in mining TiWS patterns is analyzed through a set of experiments. The proposed sequence weighting approaches may be helpful in obtaining more interesting sequential patterns in mining sequential patterns for a sequence database.
Year
DOI
Venue
2012
10.1016/j.eswa.2011.09.100
Expert Syst. Appl.
Keywords
Field
DocType
sequential pattern,weighted sequential pattern,interesting sequential pattern,sequence weighting approach,data element,time-interval weighted sequential pattern,general sequential pattern mining,generation order,mining sequential pattern,tiws pattern,comparative analysis,time-interval weighted sequential,sequence database
Data mining,Weighting,Sequence database,Pattern recognition,Computer science,Artificial intelligence,Sequential Pattern Mining
Journal
Volume
Issue
ISSN
39
3
0957-4174
Citations 
PageRank 
References 
4
0.41
13
Authors
2
Name
Order
Citations
PageRank
Joong Hyuk Chang140119.81
Nam Hun Park21057.63