Paper Info
Title
Mining weighted sequential patterns in a sequence database with a time-interval weight
Abstract
Sequential pattern mining, including weighted sequential pattern mining, has been attracting much attention since it is one of the essential data mining tasks with broad applications. The weighted sequential pattern mining aims to find more interesting sequential patterns, considering the different significance of each data element in a sequence database. In the conventional weighted sequential pattern mining, usually pre-assigned weights of data elements are used to get the importance, which are derived from their quantitative information and their importance in real world application domains. In general sequential pattern mining, the generation order of data elements is considered to find sequential patterns. However, their generation times and time-intervals are also important in real world application domains. Therefore, time-interval information of data elements can be helpful in finding more interesting sequential patterns. This paper presents a new framework for finding time-interval weighted sequential (TiWS) patterns in a sequence database and time-interval weighted support (TiW-support) to find the TiWS patterns. In addition, a new method of mining TiWS patterns in a sequence database is also presented. In the proposed framework of TiWS pattern mining, the weight of each sequence in a sequence database is first obtained from the time-intervals of elements in the sequence, and subsequently TiWS patterns are found considering the weight. A series of evaluation results shows that TIWS pattern mining is efficient and helpful in finding more interesting sequential patterns.
Year
DOI
Venue
2011
10.1016/j.knosys.2010.03.003
Knowl.-Based Syst.
Keywords
Field
DocType
sequential pattern,conventional weighted sequential pattern,weighted sequential pattern mining,time-interval sequence database,sequential pattern mining,time-interval weight,tiws pattern,tiws pattern mining,sequence database,interesting sequential pattern,weighted sequential pattern,data element,general sequential pattern mining,tiws support,data mining,generation time
Data mining,Sequence database,Pattern recognition,Data element,Computer science,Artificial intelligence,Sequential Pattern Mining
Journal
Volume
Issue
ISSN
24
1
Knowledge-Based Systems
Citations 
PageRank 
References 
26
0.73
22
Authors
1
Name
Order
Citations
PageRank
Joong Hyuk Chang140119.81