FUZZY HYPER-CLUSTERING FOR PATTERN CLASSIFICATION IN MICROARRAY GENE EXPRESSION DATA ANALYSIS

Jin Liu, Tuan D. Pham

2010

Abstract

Based on the motivation by computational challenges in microarray data analysis, we propose a fuzzy hypercluster analysis as a new framework for pattern classification using such type of data. This approach uses hyperplanes to represent the cluster centers in the fuzzy c-means algorithm. We present in this position paper the formulation of a hyperplane-based fuzzy objective function and suggest possible solutions. Fuzzy hyper-clustering approach appears to have potential as a novel alternative to analyze microarray gene expression data. Furthermore, the proposed hyper-clustering algorithm is not only confined to microarray data analysis but can be used as a general approach for classifying closely related features.

Paper Citation

in Harvard Style

Liu J. and D. Pham T. (2010). FUZZY HYPER-CLUSTERING FOR PATTERN CLASSIFICATION IN MICROARRAY GENE EXPRESSION DATA ANALYSIS . In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010) ISBN 978-989-674-018-4, pages 415-418. DOI: 10.5220/0002719504150418

in Bibtex Style

@conference{biosignals10,
author={Jin Liu and Tuan D. Pham},
title={FUZZY HYPER-CLUSTERING FOR PATTERN CLASSIFICATION IN MICROARRAY GENE EXPRESSION DATA ANALYSIS},
booktitle={Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)},
year={2010},
pages={415-418},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002719504150418},
isbn={978-989-674-018-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2010)
TI - FUZZY HYPER-CLUSTERING FOR PATTERN CLASSIFICATION IN MICROARRAY GENE EXPRESSION DATA ANALYSIS
SN - 978-989-674-018-4
AU - Liu J.
AU - D. Pham T.
PY - 2010
SP - 415
EP - 418
DO - 10.5220/0002719504150418