A Bitmap Compression Algorithm for Vertical Association Rules Mining

Abstract

The vertical association rules mining algorithm is an effective mining method recently, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantages that Apriori and its relative algorithms produce large amount of candidate itemsets and require scanning database many times. The vertical association rules mining algorithm needs to save support sets of frequent itemsets in the memory, and usually adopts bitmap to store frequent itemsets' support sets. This is the main space expense of the algorithm, and also a key factor that restricts algorithm's expansibility. Therefore, in this paper, we will present an improved algorithm which adopts compressed bitmap to improve on vertical association rules mining algorithm. It compresses the support sets which will be put into the memory to achieve the purpose of saving memory space. Our experimental results indicate that the bitmap compression algorithm for vertical association rules mining decreases memory space when the process is running.