BDDを用いたデータマイニング

概要

論文の詳細を見る
Mining for association rules in large databases is an important data, mining problem. Many algorithms have been proposed since the problem was first introduced in 1993. Basically, the procedure of mining association rules can be divided into two steps: first find out all frequently appearing sets of items (large itemsets), then caculate association rules using the large itemsets. Once all the large itemsets are obtained, the generation of association rules is quite simple. In Jiang, Inaba, Imai, we first proposed a new approach for generating large itemsets by using Binary Decision Diagram (BDD). Extensive experiments using BEM-II package are conducted to evaluate large itemset generation performance, and the results show that BDD represents and manipulates the large itemsets efficiently. In this paper, we will first review some of our previous work, then show how to improve the basic BDD algorithm to cope with the actual large database. At last, we discuss the possibility of applying BDD to sampling method. Also the relation between the prime implicants of BDD and the candidate sets will be carefully examined.
一般社団法人情報処理学会の論文
1997-09-24