Presentation 2008-06-19
Knowledge Discovery and Compression by Using Zero-suppressed BDDs
Ryutaro KURAI, Shin-ichi MINATO, Thomas ZEUGMANN,
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Abstract(in English) In the present paper we propose a new method for clustering text data by using the Normalized Compression Distance and Zero-Suppressed BDDs. The Normalized Compression Distance can be considered as an approximation of the Normalized Information Distance which is defined by using Kolmogorov complexity. Standard string compressors such as gzip, bzip2 have been previously used to compute the Normalized Compression Distance. In contrast, we propose to use the ZBDD representation of item sets as a compressor for the item sets. We conducted experiments for clustering by using our methods. The results obtained show the usefulness of this approach.
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Keyword(in English) Kolmogorov complexity / Zero-Suppressed BDD / Clustering / Text mining / Data compression
Paper # DE2008-10,PRMU2008-28
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Committee PRMU
Conference Date 2008/6/12(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Knowledge Discovery and Compression by Using Zero-suppressed BDDs
Sub Title (in English)
Keyword(1) Kolmogorov complexity
Keyword(2) Zero-Suppressed BDD
Keyword(3) Clustering
Keyword(4) Text mining
Keyword(5) Data compression
1st Author's Name Ryutaro KURAI
1st Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University()
2nd Author's Name Shin-ichi MINATO
2nd Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University
3rd Author's Name Thomas ZEUGMANN
3rd Author's Affiliation Graduate School of Information Science and Technology, Hokkaido University
Date 2008-06-19
Paper # DE2008-10,PRMU2008-28
Volume (vol) vol.108
Number (no) 94
Page pp.pp.-
#Pages 5
Date of Issue