Full Citation

Title: A Novel Multivariate Discretization Method for Mining Association Rules

Citation Type: Book, Section

Publication Year: 2009

ISBN:

ISSN:

DOI: 10.1109/APCIP.2009.102

NSFID:

PMCID:

PMID:

Abstract: Data mining aims at discovering useful patterns in large datasets. In this paper, we present a novel multivariate discretization method for finding association patterns based on clustering and genetic algorithm. This method consists of two steps. Firstly we adopt a density-based clustering technique to identify the regions that possibly hide the interesting patterns from data space. Confined to the data in these regions, we then develop a genetic algorithm to simultaneously discretize multi-attributes according to entropy criterion. The effectiveness of the proposed method is demonstrated with the experiment on a real data set.

User Submitted?: No

Authors: Wei, Hantian

Editors:

Pages: 378-381

Volume Title: Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on

Publisher: APCIP

Publisher Location: Shenzhen

Volume:

Edition:

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

Countries: