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Mining Frequent Closed Itemsets for Association Rules

Mining Frequent Closed Itemsets for Association Rules
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Author(s): Anamika Gupta (University of Delhi, India), Shikha Gupta (University of Delhi, India)and Naveen Kumar (University of Delhi, India)
Copyright: 2009
Pages: 10
Source title: Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
Source Author(s)/Editor(s): Viviana E. Ferraggine (UNICEN, Argentina), Jorge Horacio Doorn (UNICEN, Argentina)and Laura C. Rivero (UNICEN, Argentina)
DOI: 10.4018/978-1-60566-242-8.ch057

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Abstract

Association refers to correlations that exist among data. Association Rule Mining (ARM) is an important data-mining task. It refers to discovery of rules between different sets of attributes/items in very large databases (Agrawal R. & Srikant R. 1994). The discovered rules help in strategic decision making in both commercial and scientific domains. A classical application of ARM is market basket analysis, an application of data mining in retail sales where associations between the different items are discovered to analyze the customer’s buying habits in order to develop better marketing strategies. ARM has been extensively used in other applications like spatial-temporal, health care, bioinformatics, web data etc (Han J., Cheng H., Xin D., & Yan X. 2007).

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