Analysis of the data with multiple features in retail domain
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2013, Vol 2, Issue 10
Abstract
In enterprise data mining applications it is crucial to develop effective techniques for mining rules combining necessary information from multiple relevant business areas,catering for real business settings and decision-making actions rather than just providing a single line of patterns. Here we adopt combined mining as a general approach to mining for informative rules combining components of multiple features.Association rules are one of the most impressive techniques for the analysis of attribute associations in a given dataset related to applications related to retail, bioinformatics, and sociology. but often produces large collections of association rules that are difficult to understand and put into action. Through classical association mining many redundant rules are generated which may be not useful for business analysis. The proposed framework helps in generating the combined rules which gives informative knowledge for business by combining static and transactional data mining combined patterns to extract useful and actionable knowledge from a large amount of learned rules. Experimental results on retail data set demonstrate the effectiveness and potential of the proposed approach in extracting actionable knowledge from complex data.
Authors and Affiliations
Mrs. Bhavini A. Shah, Vaishali Suryawanshi
Keywords
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