Mining Frequent Patterns with Optimized Candidate Representation on GPU using Parallel Eclat Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2019, Vol 21, Issue 1
Abstract
Frequent itemset mining is one of the important aspects of association rule mining. The primary algorithm based for frequent itemset mining is mostly based on CPU and they generated a large set of items that are required to be kept in memory all the time while processing. In this dissertation thesis, we designed a parallel Eclat algorithm. The algorithm will run on GPU and perform the task of the frequentitemset mining in parallel. The proposed algorithm also uses the optimized candidate representation and the frequent item sets generated are stored in cache memory and are fetched directly from the cache memory. The proposed algorithm runs in parallel and also uses the optimized candidate representation and thus provides better performance than the classical eclat algorithm. Thus, the proposed algorithm runs much faster than the classical eclat algorithm and has better performance than classical eclat algorithm in terms of memory and time.
Authors and Affiliations
Janki Kansagra, Dr. Nilesh Kalani, Dr. Paresh Tanna
Keywords
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- EP ID EP434818
- DOI 10.9790/0661-2101011625.
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