Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11946465_4 | ISBMDA |
Keywords | Field | DocType |
multi-source bio-data,multi-clustering result,multi-source data analysis,new clustering ensemble approach,modern data mining application,integration analysis,data analysis,single source data analysis,data object,clustering result,clustering algorithm,diverse genomic data,clustering result combination,power method | Canopy clustering algorithm,Fuzzy clustering,Data mining,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Computer science,Consensus clustering,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
4345 | 0302-9743 | 3-540-68063-2 |
Citations | PageRank | References |
3 | 0.37 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hye-Sung Yoon | 1 | 34 | 2.11 |
| Sang-Ho Lee | 2 | 77 | 7.76 |
| Sung-bum Cho | 3 | 63 | 3.02 |
| Ju Han Kim | 4 | 248 | 30.80 |