Discovering significant patterns

Discovering significant patterns (2007)

by Geoffrey I. Webb

Citations:

25 - 3 self

Datum	Value	Source
TITLE	Discovering significant patterns	INFERENCE
AUTHOR NAME	Geoffrey I. Webb	SVM HeaderParse 0.2
AUTHOR AFFIL	Faculty of Information Technology	SVM HeaderParse 0.2
AUTHOR ADDR	PO Box 75, Monash University; Clayton, Vic., 3800, Australia	SVM HeaderParse 0.2
ABSTRACT	Pattern discovery techniques, such as association rule discovery, explore large search spaces of potential patterns to find those that satisfy some user-specified constraints. Due to the large number of patterns considered, they suffer from an extreme risk of type-1 error, that is, of finding patterns that appear due to chance alone to satisfy the constraints on the sample data. This paper proposes techniques to overcome this problem by applying well-established statistical practices. These allow the user to enforce a strict upper limit on the risk of experimentwise error. Empirical studies demonstrate that standard pattern discovery techniques can discover numerous spurious patterns when applied to random data and when applied to real-world data result in large numbers of patterns that are rejected when subjected to sound statistical evaluation. They also reveal that a number of pragmatic choices about how such tests are performed can greatly affect their power.	SVM HeaderParse 0.2
YEAR	2007	SVM HeaderParse 0.2
PAGES	1--33	INFERENCE
VOLUME	68	INFERENCE
CITATIONS	45 found	ParsCit 1.0