View : 56 Download: 0
Integration analysis of diverse genomic data using multi-clustering results
- Integration analysis of diverse genomic data using multi-clustering results
- Yoon H.-S.; Lee S.-H.; Cho S.-B.; Kim J.H.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 4345 LNBI, pp. 37 - 48
- Document Type
- Conference Paper
- 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.
- Appears in Collections:
- 엘텍공과대학 > 컴퓨터공학과 > Journal papers
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.