Title
Itemset based sequence classificationItemset based sequence classification
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Research group
Advanced Database Research and Modeling (ADReM)
Publication type
article
Publication
Subject
Computer. Automation
Source (journal)
Lecture notes in computer science
Volume/pages
8188(2013), p. 353-368
ISSN
0302-9743
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Sequence classification is an important task in data mining. We address the problem of sequence classification using rules composed of interesting itemsets found in a dataset of labelled sequences and accompanying class labels. We measure the interestingness of an itemset in a given class of sequences by combining the cohesion and the support of the itemset. We use the discovered itemsets to generate confident classification rules, and present two different ways of building a classifier. The first classifier is based on the CBA (Classification based on associations) method, but we use a new ranking strategy for the generated rules, achieving better results. The second classifier ranks the rules by first measuring their value specific to the new data object. Experimental results show that our classifiers outperform existing comparable classifiers in terms of accuracy and stability, while maintaining a computational advantage over sequential pattern based classification.
Handle