Parallel Eclat with Large Data Base Parallel Algorithm and Improve its Effectiveness
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2018 by IJETT Journal|
|Year of Publication : 2018|
|Authors : Ms. Shruti Ingle, Mr. Abhay Kothari
|DOI : 10.14445/22315381/IJETT-V60P227|
Ms. Shruti Ingle, Mr. Abhay Kothari"Parallel Eclat with Large Data Base Parallel Algorithm and Improve its Effectiveness", International Journal of Engineering Trends and Technology (IJETT), V60(3),180-183 June 2018. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
To better utilize the aggregate computing resources of parallel machines, a localized algorithm based on parallelization of Eclat was proposed and exhibited excellent scalability. It makes use of a vertical data layout by transforming the horizontal database transactions into vertical lists of item sets. By name, the list of an item set is a sorted list of ID’s for all transactions that contain the item set. Frequent k -item sets are organized into disjoint equivalence classes by common (k 1)-prefixes, so that candidate (k+1)-item sets can be generated by joining pairs of frequent k-item sets from the same classes. The support of a candidate item set can then be computed simply by intersecting the -lists of the two component subsets. Task parallelism is employed by dividing the mining tasks for different classes of item sets among the available processes. The equivalence classes of all frequent 2-itemsets are assigned to processes and the associated lists are distributed accordingly. Each process then mines frequent item sets generated from its assigned equivalence classes independently, by scanning and intersecting the local lists. The steps for the parallel Eclat algorithm are presented below for Distributed-memory multiprocessors divide the database evenly into horizontal partitions among all processes.
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Eclat , Data Base Parallel Algorithm