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Integration analysis of diverse genomic data using multi-clustering results

Title
Integration analysis of diverse genomic data using multi-clustering results
Authors
Yoon H.-S.Lee S.-H.Cho S.-B.Kim J.H.
Ewha Authors
이상호
SCOPUS Author ID
이상호scopus
Issue Date
2006
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
vol. 4345 LNBI, pp. 37 - 48
Indexed
SCOPUS 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. © Springer-Verlag Berlin Heidelberg 2006.
ISBN
3540680632; 9783540680635
Appears in Collections:
엘텍공과대학 > 컴퓨터공학과 > Journal papers
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