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What is Non-Redundant Association Rules

Chapter 57
Handbook of Research on Innovations in Database Technologies and Applications: Current and Future Trends
Let Ri denote the rule X1i?X2i, where X1,X2 ? I. Rule R1 is more general than rule R2 provided R2 can be generated by adding additional items to either the antecedent or consequent of R1. Rules having the same support and confidence as more general rules are the redundant association rules. Remaining rules are non-redundant rules.
Published in Chapter:
Mining Frequent Closed Itemsets for Association Rules
Anamika Gupta (University of Delhi, India), Shikha Gupta (University of Delhi, India), and Naveen Kumar (University of Delhi, India)
Copyright: © 2009 |Pages: 10
DOI: 10.4018/978-1-60566-242-8.ch057
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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Chapter 12
Algorithms for Association Rule Mining
Let Ri denote the rule X1i?X2i, where X1,X2 ? I. Rule R1 is more general than rule R2 provided R2 can be generated by adding additional items to either the antecedent or consequent of R1. Rules having the same support and confidence as more general rules are the redundant association rules. Remaining rules are non-redundant rules.
Published in Chapter: Algorithms for Association Rule Mining; From: Encyclopedia of Artificial Intelligence
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