Asian Journal of Computer Science and Technology (AJCST)
Discovering Efficient Association Rule Mining via Correlation AnalysisAuthor : C. Anuradha1 and R. Anandavally2
Volume 7 No.1 January-June 2018 pp 46-49
A Discovery of Association rule mining is an essential task in Data Mining. Traditional approaches employ a support confidence framework for finding association rule. This leads to the exploration of a number of uninteresting rules, such rules are not interesting to the users. To tackle this weakness, this paper examines the correlation measures to augment with support and confidence framework, which resulting in the mining of correlation rules. We then added an additional interesting measure based on statistical significance and correlation analysis. This paper reveals an overview of interesting measures and gives an insight into the discovery of more meaningful rules from large applications than traditional approach. Also it covers a theoretical issues associated with correlations that have yet to be explored.
Correlation, Cosine, null-invariant, support-confidence framework
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