Cluster validity for DNA micro array data using a geometrical index

Benson S. Y. Lam; Hong Van

Cluster validity for DNA micro array data using a geometrical index

Benson S. Y. Lam
, Hong Van

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

7 Citations (Scopus)

Abstract

A number of clustering methods have been used for DNA microarray data analysis. It is an important problem how to evaluate the results of these algorithms. In this paper, we introduce a new cluster validity index, which measures the geometrical feature of the data. The essential concept of this index is to measure the squared total length of the data eigenaxes with respect to the between-cluster separation. We show that this cluster validity index works well for data, which are close together or have different sizes. In our experiments, the proposed index is compared to five other validity indices and experiment results show that the proposed index gives more accurate results. ©2005 IEEE.

Original language	English
Title of host publication	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Pages	3333-3339
Publication status	Published - 2005
Event	International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China Duration: 18 Aug 2005 → 21 Aug 2005

Publication series

Name	2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

Conference

Conference	International Conference on Machine Learning and Cybernetics, ICMLC 2005
Place	China
City	Guangzhou
Period	18/08/05 → 21/08/05

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

The work described in this paper is fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (project CityU 1035/02E).

Research Keywords

Cluster Validity
DNA microarray Data Analysis
Pattern Classification

RGC Funding Information

RGC-funded

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@inproceedings{1fef27014cec4bf0bd7795136d6bc01b,

title = "Cluster validity for DNA micro array data using a geometrical index",

abstract = "A number of clustering methods have been used for DNA microarray data analysis. It is an important problem how to evaluate the results of these algorithms. In this paper, we introduce a new cluster validity index, which measures the geometrical feature of the data. The essential concept of this index is to measure the squared total length of the data eigenaxes with respect to the between-cluster separation. We show that this cluster validity index works well for data, which are close together or have different sizes. In our experiments, the proposed index is compared to five other validity indices and experiment results show that the proposed index gives more accurate results. {\textcopyright}2005 IEEE.",

keywords = "Cluster Validity, DNA microarray Data Analysis, Pattern Classification",

author = "Lam, \{Benson S. Y.\} and Hong Van",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; International Conference on Machine Learning and Cybernetics, ICMLC 2005 ; Conference date: 18-08-2005 Through 21-08-2005",

year = "2005",

language = "English",

isbn = "078039092",

series = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

pages = "3333--3339",

booktitle = "2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005",

}

Lam, BSY & Van, H 2005, Cluster validity for DNA micro array data using a geometrical index. in 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005, pp. 3333-3339, International Conference on Machine Learning and Cybernetics, ICMLC 2005, Guangzhou, China, 18/08/05.

Cluster validity for DNA micro array data using a geometrical index. / Lam, Benson S. Y.; Van, Hong.
2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005. 2005. p. 3333-3339 (2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Cluster validity for DNA micro array data using a geometrical index

AU - Lam, Benson S. Y.

AU - Van, Hong

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2005

Y1 - 2005

N2 - A number of clustering methods have been used for DNA microarray data analysis. It is an important problem how to evaluate the results of these algorithms. In this paper, we introduce a new cluster validity index, which measures the geometrical feature of the data. The essential concept of this index is to measure the squared total length of the data eigenaxes with respect to the between-cluster separation. We show that this cluster validity index works well for data, which are close together or have different sizes. In our experiments, the proposed index is compared to five other validity indices and experiment results show that the proposed index gives more accurate results. ©2005 IEEE.

AB - A number of clustering methods have been used for DNA microarray data analysis. It is an important problem how to evaluate the results of these algorithms. In this paper, we introduce a new cluster validity index, which measures the geometrical feature of the data. The essential concept of this index is to measure the squared total length of the data eigenaxes with respect to the between-cluster separation. We show that this cluster validity index works well for data, which are close together or have different sizes. In our experiments, the proposed index is compared to five other validity indices and experiment results show that the proposed index gives more accurate results. ©2005 IEEE.

KW - Cluster Validity

KW - DNA microarray Data Analysis

KW - Pattern Classification

UR - https://www.scopus.com/pages/publications/28444483613

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-28444483613&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 078039092

SN - 9780780390928

T3 - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

SP - 3333

EP - 3339

BT - 2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005

T2 - International Conference on Machine Learning and Cybernetics, ICMLC 2005

Y2 - 18 August 2005 through 21 August 2005

ER -

Cluster validity for DNA micro array data using a geometrical index

Abstract

Publication series

Conference

Bibliographical note

Funding

Research Keywords

RGC Funding Information

Other Access Options

Permanent Link

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Cite this