An Efficiently Algorithm Based on Itemsets-Lattice and Bitmap Index for Finding Frequent Itemsets

Abstract

AbstractFrequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases. A new algorithm based on the lattice theory and bitmap index for mining frequent itemsets is proposed in this paper. Firstly, the algorithm converts the origin transaction database to an itemsets-lattice (which is a directed graph) in the preprocessing, where each itemset vertex has a label to represent its support. So we can change the complicated task of mining frequent itessets in the database to a simpler one of searching vertexes in the lattice, which can speeds up greatly the mining process. Secondly, Support counting in the association rules mining requires a great I/O and computing cost. A bitmap index technique to speed up the counting process is employed in this paper. Saving the intact bitmap usually has a big space requirement. Each bit vector is partitioned into some blocks, and hence every bit block is encoded as a shorter symbol. Therefore the original bitmap is impacted efficiently. At the end experimental and analytical results are presented.KeywordsAssociation RuleMain MemoryFrequent ItemsetsAssociation Rule MiningSupport ThresholdThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.