Space decomposition in data mining: A clustering approach

Lior Rokach, Oded Maimon, Inbal Lavi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

27 Scopus citations


Data mining algorithms aim at searching interesting patterns in large amount of data in manageable complexity and good accuracy. Decomposition methods are used to improve both criteria. As opposed to most decomposition methods, that partition the dataset via sampling, this paper presents an accuracyoriented method that partitions the instance space into mutually exclusive subsets using K-means clustering algorithm. After employing the basic divide-andinduce method on several datasets with different classifiers, its error rate is compared to that of the basic learning algorithm. An analysis of the results shows that the proposed method is well suited for datasets of numeric input attributes and that its performance is influenced by the dataset size and its homogeneity. Finally, a homogeneity threshold is developed, that can be used for deciding whether to decompose the data set or not.

Original languageEnglish
Title of host publicationFoundations of Intelligent Systems - 14th International Symposium, ISMIS 2003, Proceedings
EditorsNing Zhong, Zbigniew W. Ras, Shusaku Tsumoto, Einoshin Suzuki
PublisherSpringer Verlag
Number of pages8
ISBN (Print)3540202560, 9783540202561
StatePublished - 2003
Event14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003 - Maebashi City, Japan
Duration: 28 Oct 200331 Oct 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003
CityMaebashi City


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