Pruning and Summarizing the Discovered Associations

Pruning and Summarizing the Discovered Associations (1999)

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by Bing Liu , Wynne Hsu , Yiming Ma

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Datum	Value	Source
TITLE	Pruning and Summarizing the Discovered Associations	user correction - Legacy Corrections
AUTHOR NAME	Bing Liu	SVM HeaderParse 0.1
AUTHOR AFFIL	School of Computing; National University of Singapore	SVM HeaderParse 0.2
AUTHOR ADDR	Lower Kent Ridge Road, Singapore 119260	SVM HeaderParse 0.1
AUTHOR NAME	Wynne Hsu	SVM HeaderParse 0.1
AUTHOR AFFIL	School of Computing; National University of Singapore	SVM HeaderParse 0.2
AUTHOR ADDR	Lower Kent Ridge Road, Singapore 119260	SVM HeaderParse 0.1
AUTHOR NAME	Yiming Ma	SVM HeaderParse 0.1
AUTHOR AFFIL	School of Computing; National University of Singapore	SVM HeaderParse 0.2
AUTHOR ADDR	Lower Kent Ridge Road, Singapore 119260	SVM HeaderParse 0.1
ABSTRACT	Association rules are a fundamental class of patterns that exist in data. The key strength of association rule mining is its completeness. It finds all associations in the data that satisfy the user specified minimum support and minimum confidence constraints. This strength, however, comes with a major drawback. It often produces a huge number of associations. This is particularly true for data sets whose attributes are highly correlated. The huge number of associations makes it very difficult, if not impossible, for a human user to analyze in order to identify those interesting/useful ones. In this paper, we propose a novel technique to overcome this problem. The technique first prunes the discovered associations to remove those insignificant associations, and then finds a special subset of the unpruned associations to form a summary of the discovered associations. We call this subset of associations the direction setting (DS) rules as they set the directions that are followed by the...	user correction - Legacy Corrections
YEAR	1999	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	125--134	INFERENCE
CITATIONS	26 found	ParsCit 1.0