Integration analysis of diverse genomic data using multi-clustering results

Hye Sung Yoon, Sang Ho Lee, Sung Bum Cho, Ju Han Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

3 Citations (Scopus)

Abstract

In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the complexity of data objects, is a much more powerful method than evaluating each source separately. Therefore, a new paradigm is required that combines the genome-wide experimental results of multi-source datasets. However, multi-source data analysis is more difficult than single source data analysis. In this paper, we propose a new clustering ensemble approach for multi-source bio-data on complex objects. In addition, we present encouraging clustering results in a real bio-dataset examined using our proposed method.

Original languageEnglish
Title of host publicationBiological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings
Pages37-48
Number of pages12
Volume4345 LNBI
StatePublished - 1 Dec 2006
Event7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006 - Thessaloniki, Greece
Duration: 7 Dec 20068 Dec 2006

Other

Other7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006
CountryGreece
CityThessaloniki
Period7/12/068/12/06

Fingerprint

Genomics
Clustering
Data analysis
Clustering algorithms
Data mining
Labels
Ensemble
Genes
Clustering Algorithm
Data Mining
Genome
Paradigm
Optimise
Experimental Results
Object

Cite this

Yoon, H. S., Lee, S. H., Cho, S. B., & Kim, J. H. (2006). Integration analysis of diverse genomic data using multi-clustering results. In Biological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings (Vol. 4345 LNBI, pp. 37-48)
Yoon, Hye Sung ; Lee, Sang Ho ; Cho, Sung Bum ; Kim, Ju Han. / Integration analysis of diverse genomic data using multi-clustering results. Biological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings. Vol. 4345 LNBI 2006. pp. 37-48
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Yoon, HS, Lee, SH, Cho, SB & Kim, JH 2006, Integration analysis of diverse genomic data using multi-clustering results. in Biological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings. vol. 4345 LNBI, pp. 37-48, 7th International Symposium on Biological and Medical Data Analysis, ISBMDA 2006, Thessaloniki, Greece, 7/12/06.

Integration analysis of diverse genomic data using multi-clustering results. / Yoon, Hye Sung; Lee, Sang Ho; Cho, Sung Bum; Kim, Ju Han.

Biological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings. Vol. 4345 LNBI 2006. p. 37-48.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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AB - In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conducting data analysis using multiple sources, given the complexity of data objects, is a much more powerful method than evaluating each source separately. Therefore, a new paradigm is required that combines the genome-wide experimental results of multi-source datasets. However, multi-source data analysis is more difficult than single source data analysis. In this paper, we propose a new clustering ensemble approach for multi-source bio-data on complex objects. In addition, we present encouraging clustering results in a real bio-dataset examined using our proposed method.

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Yoon HS, Lee SH, Cho SB, Kim JH. Integration analysis of diverse genomic data using multi-clustering results. In Biological and Medical Data Analysis - 7th International Symposium, ISBMDA 2006, Proceedings. Vol. 4345 LNBI. 2006. p. 37-48