人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
速報論文
ZDDを用いた効率的な集合拡張の計算
西野 正彬安田 宜仁小林 透
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2012 年 27 巻 2 号 p. 22-27

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Set Expansion is a method for finding similar sets of items from a seed set. It is useful on examining a large set of items to find hidden relationships among the items. In this paper, we propose a method that can reduce the number of calculations needed on executing Bayesian Sets, which is one of the most popular set expansion algorithms. The key point of our method lies in the use of Zero-suppressed Binary Decision Diagrams (ZDD) to express a binary value sparse matrix in a compressed form and executing needed calculations directly on constructed ZDD. We show a method for expressing a binary value matrix with ZDD, and also show some techniques for reducing the size of ZDD. We confirm the effectiveness of our method with experiments on both synthesis and real data.

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© 2012 JSAI (The Japanese Society for Artificial Intelligence)
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