The week's pick
Reseach Article
A Search Space Reduction Algorithm for Mining Maximal Frequent Itemset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 82 - Number 9 |
| Year of Publication: 2013 |
| Authors: K. Sumathi, S. Kannan, K. Nagarajan |
10.5120/14146-2288
|
K. Sumathi, S. Kannan, K. Nagarajan . A Search Space Reduction Algorithm for Mining Maximal Frequent Itemset. International Journal of Computer Applications. 82, 9 ( November 2013), 32-36. DOI=10.5120/14146-2288
Abstract
Abstract -Mining of frequent itemset plays important role in data mining applications. The algorithms which are used to generate the frequent patterns must perform efficiently. Because the overall performance of association rule mining based on fast discovery of frequent pattern. Many MFI approaches need to recursively construct many candidates, they also suffer the problem of a large search space, so that the performances for the approaches degrade when the database is massive or the threshold for mining frequent patterns is low. In this paper, an efficient method for discovering the maximal frequent itemsets is proposed which combines a vertical tidset representation of the database with effective pruning mechanisms for search space reduction. It works efficiently when the number of itemsets and tidsets are more. The proposed approach has been compared with GenMax algorithm for mushroom dataset and the results show that the proposed algorithm generates less number of candidate itemsets from which MFIs are obtained. Hence, the proposed algorithm performs effectively and generates maximal frequent patterns faster.
References
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