A frequent itemset mining algorithm based on composite granular computing

Abstract

This paper proposes a frequent itemset mining algorithm based on the divide and conquer strategy in composite granular computing. In order to construct composite information granules (CIGs) and find the frequent patterns, an iterative approach is used in this algorithm. First, create atomic informa tion granules. Next, atomic composite information granules are generated by atomic information granules. Then, through the intersect operation between atomic composite information granules and prune action, the frequent 2-CIGs that will be used to construct frequent 3-CIGs will be constructed, and so on, until no more frequent CIGs can be found. When creating CIGs, this method will improve the computing speed by logical operation in binary. It can avoid scanning database frequently and avoid using complex data structure, so it will reduce the I/O overhead and save a lot of memory space. And it also can optimize the generation of candidate CIGs and compress the transaction database dynamically. The experimental results show that this algorithm has good performance and has low computational complexity and high efficiency.