Logical analysis of data with decomposable structures

Hirotaka Ono, Kazuhisa Makino, Toshihide Ibaraki

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

In such areas as knowledge discovery, data mining and logical analysis of data, methodologies to find relations among attributes are considered important. In this paper, given a data set (T,F) where T ⊆ {0,1}n denotes a set of positive examples and F⊆{0,1}n denotes a set of negative examples, we propose a method to identify decomposable structures among the attributes of the data. We first study computational complexity of the problem of finding decomposable Boolean extensions. Since the problem turns out to be intractable (i.e., NP-complete), we propose a heuristic algorithm in the second half of the paper. Our method searches a decomposable partition of the set of all attributes by using the error sizes of almost-fit decomposable extensions as a guiding measure, and then finds structural relations among the attributes in the obtained partition. Some results of numerical experiment on randomly generated data sets are also reported.

Original languageEnglish
Pages (from-to)977-995
Number of pages19
JournalTheoretical Computer Science
Volume289
Issue number2
DOIs
Publication statusPublished - Oct 30 2002
EventComputing and Combinatorics (COCOON 2000) - Sydney, NSW, Australia
Duration: Jul 1 2000Jul 1 2000

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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