关键词: 频繁闭项目集, 粒度计算, 数据挖掘

Abstract: Aiming to these shortcomings from the present frequent closed itemsets mining algorithms, this paper proposes an algorithm of frequent closed itemsets mining based on granular computing. The algorithm uses the varying mixed radix number to generate candidate itemsets, and avoids adopting the complex data structure to reduce the memory and the CPU overhead. And it uses divide and rule for granular computing to compute the support of frequent closed itemsets, and avoids reading repeatedly the database to reduce the computation complexity and I/O overhead. These experimental results indicate that the algorithm is faster and more efficient than these classical mining algorithms for frequent closed itemsets.

Key words: frequent closed itemsets, granular computing, data mining