出版社：The Japanese Society for Artificial Intelligence
摘要：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.
