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| A Performance Based Transposition algorithm for Frequent Itemsets Generation
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Full
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Source |
International Journal of Data Engineering (IJDE) |
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Table of Contents |
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Complete Issue PDF(2.52MB) |
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Volume: 2 Issue: 2 |
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Pages: 27-92 |
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Publication
Date: May / June 2011 |
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ISSN
(Online): 2180-1274 |
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Pages |
53 - 61 |
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Author(s) |
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Published
Date |
31-05-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Data Mining, Association Rule Mining(ARM), Association Rules |
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| This Manuscript is indexed in the following databases/websites:- |
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| Association Rule Mining (ARM) technique is used to discover the interesting association or correlation among a large set of data items. it plays an important role in generating frequent itemsets from large databases. Many industries are interested in developing the association rules from their databases due to continuous retrieval and storage of huge amount of data. The discovery of interesting association relationship among business transaction records in many business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. It is also used to extract hidden knowledge from large datasets. The ARM algorithms such as Apriori, FP-Growth requires repeated scans over the entire database. All the input/output overheads that are being generated during repeated scanning the entire database decrease the performance of CPU, memory and I/O overheads. In this paper, we have proposed a Performance Based Transposition Algorithm (PBTA) for frequent itemsets generation. We will compare proposed algorithm with Apriori algorithm for frequent itemsets generation. The CPU and I/O overhead can be reduced in our proposed algorithm and it is much faster than other ARM algorithms.
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| Sanjeev Kumar Sharma : Colleagues
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| Ugrasen Suman : Colleagues
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