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Mining Literal Correlation Rules from Itemsets

Authors:
Alain Casali
Christian Ernst

Keywords: Data Mining; Chi2 Correlation Statistic; Literal Pattern.

Abstract:
Nowadays, data mining tools are becoming more and more popular to extract knowledge from a huge volume of data. In this paper, our aim is to extract Literal Correlation Rules: Correlation Rules admitting literal patterns given a set of items and a binary relation. If a pattern represents a valid Correlation Rule, then any literal belonging to its Canonical Base represents a valid Literal Correlation Rule. Moreover, in order to highlight only relevant Literal Correlation Rules, we add a pruning step based on a support threshold. To extract such rules, we modify the LHS-CHI2 Algorithm and perform some experiments.

Pages: 162 to 167

Copyright: Copyright (c) IARIA, 2011

Publication date: October 23, 2011

Published in: conference

ISSN: 2326-9332

ISBN: 978-1-61208-162-5

Location: Barcelona, Spain

Dates: from October 23, 2011 to October 29, 2011