Symmetric Item Set Mining Method Using Zero-suppressed BDDs and Application to Biological Data

Minato, Shin-ichi; Ito, Kimihito

Transactions of the Japanese Society for Artificial Intelligence, 2007, 22(2), 156-164

Permalink : https://hdl.handle.net/2115/47338

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アクセス権
open access
URI
https://hdl.handle.net/2115/47338
タイトル
Symmetric Item Set Mining Method Using Zero-suppressed BDDs and Application to Biological Data
著者
e-Rad_Researcher
10374612
著者名
Minato, Shin-ichi
著者名
Ito, Kimihito
言語
英語
キーワード
data mining
item set
BDD
ZBDD
biological database
発行日
2007
出版者
人工知能学会
収録物名
Transactions of the Japanese Society for Artificial Intelligence
人工知能学会論文誌
巻(号)
22(2)
ページ
156-164
抄録
In this paper, we present a method of finding symmetric items in a combinatorial item set database. The techniques for finding symmetric variables in Boolean functions have been studied for long time in the area of VLSI logic design, and the BDD (Binary Decision Diagram) -based methods are presented to solve such a problem. Recently, we have developed an efficient method for handling databases using ZBDDs (Zero-suppressed BDDs), a particular type of BDDs. In our ZBDD-based data structure, the symmetric item sets can be found efficiently as well as for Boolean functions. We implemented the program of symmetric item set mining, and applied it to actual biological data on the amino acid sequences of influenza viruses. We found a number of symmetric items from the database, some of which indicate interesting relationships in the amino acid mutation patterns. The result shows that our method is helpful for extracting hidden interesting information in real-life databases.
資源タイプ
学術雑誌論文
出版タイプ
Version of Record
PISSN
1346-0714
EISSN
1346-8030
関連情報 (isIdenticalTo)
出版社版DOI
https://doi.org/10.1527/tjsai.22.156