Model for Load Balancing on Processors in Parallel Mining of Frequent Itemsets‪.‬

Abstract: The existence of many large transactional distributed databases with high data skewness, the centralized approach for mining association rules in such databases will not be feasible. Some distributed algorithms have been developed [FDM, CD], but none of them have considered the problem of data skewness in distributed mining of association rules. The skewness of datasets reduces the workload balancing between processors involved in distributed mining of association rules. It is important to invent efficient approach for distributed mining of association rules which have ability to generate homogeneous partitions of the whole datasets; hence the supports of most large itemsets are distributed evenly across the processors. We proposed an efficient stratified sampling based partitioned technique, which generate homogeneous partitions on which processors works in parallel and generate their local concepts approximately simultaneously. Keywords: Association rules, Data Mining, Data Skewness, Workload Balance, Parallel Mining, Partitionine and Stratified Samolina