Generate frequent item set in serial and parallel approach |
Author(s): |
| Rinku kadam , P K Technical Campus; Gawde Renuka, P K Technical Campus; Kumbhar Dhanashree, P K Technical Campus; Londhe Dipali, P K Technical Campus |
Keywords: |
| Parallel Data Mining, Frequent Item Sets, Association Rules, Apriori Algorithm, Data mining, parallel processing, apriori algorithm. |
Abstract |
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Apriori Algorithms are used on very huge data sets with high dimensionality. Therefore parallel computing can be used for mining of association rules. The process of association rule mining consists of finding frequent itemsets and forming rules from the frequent itemsets. Finding frequent item sets is more expensive in terms of power and resources utilization. Thus majority of parallel apriori algorithms focus on parallelizing the process of frequent itemset formation. The computation of frequent itemsets mainly consists of creating the candidate keys and counting them. The parallel frequent item sets mining algorithms addresses the issue of distributing the candidate keys among processors such that their counting and creation is effectively parallelized. This paper presents comparative analysis of these algorithms. |
Other Details |
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Paper ID: IJSRDV3I90037 Published in: Volume : 3, Issue : 9 Publication Date: 01/12/2015 Page(s): 49-52 |
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