Paper Info
Title
Condensed Representation of Sequential Patterns According to Frequency-Based Measures
Abstract
Condensed representations of patterns are at the core of many data mining works and there are a lot of contributions handling data described by items. In this paper, we tackle sequential data and we define an exact condensed representation for sequential patterns according to the frequency-based measures. These measures are often used, typically in order to evaluate classification rules. Furthermore, we show how to infer the best patterns according to these measures, i.e., the patterns which maximize them. These patterns are immediately obtained from the condensed representation so that this approach is easily usable in practice. Experiments conducted on various datasets demonstrate the feasibility and the interest of our approach.
Year
DOI
Venue
2009
10.1007/978-3-642-03915-7_14
IDA
Keywords
Field
DocType
sequential pattern,frequency-based measure,frequency-based measures,condensed representation,exact condensed representation,data mining work,various datasets,classification rule,best pattern,sequential data,data mining
USable,Sequential data,Data mining,Pattern recognition,Computer science,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
5772
0302-9743
10
PageRank 
References 
Authors
0.70
19
2
Name
Order
Citations
PageRank
Marc Plantevit123330.78
Bruno Crémilleux237334.98