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Output details

11 - Computer Science and Informatics

Manchester Metropolitan University

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Output 22 of 37 in the submission

Output title

Fuzzy weighted association rule mining with weighted support and confidence framework

Type

E - Conference contribution

DOI

10.1007/978-3-642-00399-8

Name of conference/published proceedings

New Frontiers in Applied Data Mining. Lecture Notes in Computer Science

Volume number

5433

Issue number

First page of article

ISSN of proceedings

1611-3349

Year of publication

2009

URL

Number of additional authors

Additional information

<15>We describe a new framework for the efficient processing of fuzzy weighted association rules, and introduce a weighted downward closure property (DCP). Our algorithm avoids the pre- and post processing required by most weighted association rule mining algorithms in the existing literature. A significant contribution is the modelling of fuzzy weighted association rules by fuzzy sets, and exposition of known issues concerning DCP in frequent pattern mining. Rigorous experiments demonstrate the scalability of the approach, and show how it outperforms various standard algorithms. Potential further work would address value normalisation of fuzzy weights and other measures for DCP.

Interdisciplinary

Cross-referral requested

Research group

C - Networks and Distributed Systems

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract