疑似頻出アイテム集合の多項式遅延列挙アルゴリズム

抄録

Mining frequently appearing patterns in a database is a basic problem in recent informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, the problem is called frequent itemset mining problem, and has been extensively studied. In the real world use, one of popular difficulties of frequent itemset mining is that data is often incorrect, or missing some parts. It causes that some records which should/may include a pattern become not including the pattern. Thus, in real world problems, it is valuable to use an ambiguous inclusion relation and find patterns which is "almost" included in many records. However, from the difficulty on the computation, such kind of problems have not been actively studied. In this paper, we use an alternative inclusion relation in which we consider an itemset P is included in an itemset T if at most k items of P are not included in T, i.e., |P ＼ T| <_ k. We address the problem of enumerating frequent itemset under this inclusion and propose an effi-cient polynomial delay polynomial space algorithm. To skip so many small non-valuable frequent itemsets, we propose an algorithm for directly enumerating frequent itemsets of a certain size.