DISTRIBUTED MINING ADJUSTABLE ACCURACY ASSOCIATION RULES USING SAMPLING

Association rule mining is an important task of data mining. A fast distributed algorithm for mining adjustable accuracy association rules is presented using sampling and meta learning. In order to acquire more complete results, several variants of the algorithm are also discussed by selecting smaller minimum support and extending global candidate itemsets. The method is compared with similar algorithms. The algorithm is more efficient and has less communicated loads, applicable to those applications where the efficiency could be more important than accuracy results.