Integration analysis of diverse genomic data using multi-clustering results

Authors:
Hye-Sung Yoon;Sang-Ho Lee;Sung-Bum Cho;Ju Han Kim
Affiliations:
Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea;Department of Computer Science and Engineering, Ewha Womans University, Seoul, Korea;Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea;Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul, Korea
Venue:
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Year:
2006

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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.