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Extraction of High Utility Itemsets using Utility Pattern with Genetic Algorithm from OLTP System

, and . International Journal on Recent and Innovation Trends in Computing and Communication 3 (3): 1326--1331 (March 2015)

Abstract

To analyse vast amount of data, Frequent pattern mining play an important role in data mining. In practice, Frequent pattern mining cannot meet the challenges of real world problems due to items differ in various measures. Hence an emerging technique called Utility-based data mining is used in data mining processes.The utility mining not only considers the frequency but also see the utility associated with the itemsets.The main objective of utility mining is to extract the itemsets with high utilities, by considering user preferences such as profit,quantity and cost from OLTP systems. In our proposed approach, we are using UP growth with Genetic Algorithm. The idea is that UP growth algorithm would generate Potentially High Utility Itemsets and Genetic Algorithm would optimize and provide the High Utility Item set from it. On comparing with existing algorithm, the proposed approach is performing better in terms of memory utilization

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DOI:
10.17762/ijritcc2321-8169.150394
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BibTeX key:
Saranya_2015
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