A novel parameter-less clustering method for mining gene expression data

S. Tseng; Ching Pin Kao

doi:10.1007/978-3-540-24775-3_81

A novel parameter-less clustering method for mining gene expression data

S. Tseng, Ching Pin Kao

Institute of Data Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Clustering analysis has been applied in a wide variety of fields. In recent years, it has even become a valuable and useful technique for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in the requirements of automation, high quality, and high efficiency at the same time. In this paper, we explore the issue of integration between clustering methods and validation techniques. We propose a novel, parameter-less, and efficient clustering algorithm, namely CST, which is suitable for analysis of gene expression data. Through experimental evaluation, CST is shown to outperform other clustering methods substantially in terms of clustering quality, efficiency, and automation under various types of datasets.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Chengqi Zhang, Ramakrishnan Srikant
Publisher	Springer Verlag
Pages	692-698
Number of pages	7
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_81
State	Published - 1 Jan 2004
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country	Australia
City	Sydney
Period	26/05/04 → 28/05/04

Access to Document

10.1007/978-3-540-24775-3_81

Link to publication in Scopus

Fingerprint Dive into the research topics of 'A novel parameter-less clustering method for mining gene expression data'. Together they form a unique fingerprint.

Cite this

Tseng, S., & Kao, C. P. (2004). A novel parameter-less clustering method for mining gene expression data. In H. Dai, C. Zhang, & R. Srikant (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 692-698). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_81

Tseng, S. ; Kao, Ching Pin. / A novel parameter-less clustering method for mining gene expression data. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Chengqi Zhang ; Ramakrishnan Srikant. Springer Verlag, 2004. pp. 692-698 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3c261ee0486749d1bf35197c0e6b1141,

title = "A novel parameter-less clustering method for mining gene expression data",

abstract = "Clustering analysis has been applied in a wide variety of fields. In recent years, it has even become a valuable and useful technique for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in the requirements of automation, high quality, and high efficiency at the same time. In this paper, we explore the issue of integration between clustering methods and validation techniques. We propose a novel, parameter-less, and efficient clustering algorithm, namely CST, which is suitable for analysis of gene expression data. Through experimental evaluation, CST is shown to outperform other clustering methods substantially in terms of clustering quality, efficiency, and automation under various types of datasets.",

author = "S. Tseng and Kao, {Ching Pin}",

year = "2004",

month = jan,

day = "1",

doi = "10.1007/978-3-540-24775-3_81",

language = "English",

isbn = "354022064X",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "692--698",

editor = "Honghua Dai and Chengqi Zhang and Ramakrishnan Srikant",

booktitle = "Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings",

address = "Germany",

note = "null ; Conference date: 26-05-2004 Through 28-05-2004",

}

Tseng, S & Kao, CP 2004, A novel parameter-less clustering method for mining gene expression data. in H Dai, C Zhang & R Srikant (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 692-698, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_81

A novel parameter-less clustering method for mining gene expression data. / Tseng, S.; Kao, Ching Pin.

Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Chengqi Zhang; Ramakrishnan Srikant. Springer Verlag, 2004. p. 692-698 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A novel parameter-less clustering method for mining gene expression data

AU - Tseng, S.

AU - Kao, Ching Pin

PY - 2004/1/1

Y1 - 2004/1/1

N2 - Clustering analysis has been applied in a wide variety of fields. In recent years, it has even become a valuable and useful technique for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in the requirements of automation, high quality, and high efficiency at the same time. In this paper, we explore the issue of integration between clustering methods and validation techniques. We propose a novel, parameter-less, and efficient clustering algorithm, namely CST, which is suitable for analysis of gene expression data. Through experimental evaluation, CST is shown to outperform other clustering methods substantially in terms of clustering quality, efficiency, and automation under various types of datasets.

AB - Clustering analysis has been applied in a wide variety of fields. In recent years, it has even become a valuable and useful technique for in-silico analysis of microarray or gene expression data. Although a number of clustering methods have been proposed, they are confronted with difficulties in the requirements of automation, high quality, and high efficiency at the same time. In this paper, we explore the issue of integration between clustering methods and validation techniques. We propose a novel, parameter-less, and efficient clustering algorithm, namely CST, which is suitable for analysis of gene expression data. Through experimental evaluation, CST is shown to outperform other clustering methods substantially in terms of clustering quality, efficiency, and automation under various types of datasets.

UR - http://www.scopus.com/inward/record.url?scp=7444245697&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-24775-3_81

DO - 10.1007/978-3-540-24775-3_81

M3 - Conference contribution

AN - SCOPUS:7444245697

SN - 354022064X

SN - 9783540220640

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 692

EP - 698

BT - Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings

A2 - Dai, Honghua

A2 - Zhang, Chengqi

A2 - Srikant, Ramakrishnan

PB - Springer Verlag

Y2 - 26 May 2004 through 28 May 2004

ER -

Tseng S, Kao CP. A novel parameter-less clustering method for mining gene expression data. In Dai H, Zhang C, Srikant R, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 692-698. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-24775-3_81