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Symmetric Item Set Mining Method Using Zero-suppressed BDDs and Application to Biological Data
Title: | Symmetric Item Set Mining Method Using Zero-suppressed BDDs and Application to Biological Data |
Authors: | Minato, Shin-ichi Browse this author →KAKEN DB | Ito, Kimihito Browse this author |
Keywords: | data mining | item set | BDD | ZBDD | biological database |
Issue Date: | 2007 |
Publisher: | 人工知能学会 |
Journal Title: | Transactions of the Japanese Society for Artificial Intelligence |
Journal Title(alt): | 人工知能学会論文誌 |
Volume: | 22 |
Issue: | 2 |
Start Page: | 156 |
End Page: | 164 |
Publisher DOI: | 10.1527/tjsai.22.156 |
Abstract: | 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. |
Type: | article |
URI: | http://hdl.handle.net/2115/47338 |
Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 湊 真一
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