A New Dynamic Distributed Algorithm for Frequent Itemsets Mining

Abstract

Mining for association rules between items in large transactional databases is a central problem in the field of knowledge discovery. It has crucial applications in decision support and marketing strategy. Centralized and Distributed Association Rules Mining (DARM) include two phases of frequent itemset extraction and strong rule generation. The most important part of ARM is Frequent Itemsets Mining (FIM)and because of its importance in recent years, there have been many algorithms implemented for it. In this paper, we have focused on distributed Apriori-Like frequent itemsets mining and proposed a distributed algorithm, called New Dynamic Distributed Frequent Itemsets Mining (NDD-FIM), for geographically distributed data sets. NDD-FIM has a merger site to reduce communication overhead and eliminates size of dataset partitions dynamically. The experimental results show that our algorithm generates support counts of candidate itemsets quickerthan other DARM algorithms and reduces the size of average transactions, datasets, and messageexchanges.

Parallel Pre-processing for XML mining using Graphic Processor

“…Efficiency and processing ability is greatly enhanced and is suitable for large data. Azam Adelpoor et al emphasizes on distributed version of Apriori-Like frequent itemsets mining [1]. They have proposed New Dynamic Distributed Frequent Itemsets Mining (NDD-FIM) which applies a distributed approach for data sets that are geographically distributed.…”

“…In an XML document, the nodes or node-sets are selected using XPath. The main principle of the XPath 2.0 is to address nodes in an XML document, which is the same as for the 1 GPUs are extremely multi-threaded multiple core processors. It has attracted the researches because of easy availability, low cost and energy efficiency [12].…”

“…Many attempts have also been done to parallelize the association rule mining process [5], [16], [1], [2]. A transaction database distribution scheme was introduced in [5] that use the approach of dividing the frequent item set mining task in a top-down fashion.…”