Paper Info
Title
Local Pattern Mining from Sequences Using Rough Set Theory
Abstract
Sequential pattern mining is a crucial but challenging task in many applications, e.g., analyzing the behaviors of data in transactions and discovering frequent patterns in time series data. This task becomes difficult when valuable patterns are locally or implicitly involved in noisy data. In this paper, we propose a method for mining such local patterns from sequences. Using rough set theory, we describe an algorithm for generating decision rules that take into account local patterns for arriving at a particular decision. To apply sequential data to rough set theory, the size of local patterns is specified, allowing a set of sequences to be transformed into a sequential information system. We use the discernibility of decision classes to establish evaluation criteria for the decision rules in the sequential information system.
Year
DOI
Venue
2010
10.1109/GrC.2010.49
GrC
Keywords
Field
DocType
rough set theory,decision rule,decision rules,sequential data,local pattern mining,decision class,sequential information system,data mining,time series data,local pattern,sequential pattern mining,particular decision,noisy data,set theory,algorithm design and analysis,noise measurement,accuracy,information system
Information system,Time series,Data mining,Noise measurement,Computer science,Artificial intelligence,Dominance-based rough set approach,Decision rule,Set theory,Algorithm design,Pattern recognition,Rough set,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-7964-1
1
0.37
References 
Authors
3
2
Name
Order
Citations
PageRank
Ken Kaneiwa127024.26
Yasuo Kudo29526.41