Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Information retrieval
ACM Computing Surveys (CSUR)
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Two-phase clustering strategy for gene expression data sets
Proceedings of the 2006 ACM symposium on Applied computing
A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Wavelet transformation and cluster ensemble for gene expression analysis
International Journal of Bioinformatics Research and Applications
GE-Miner: integration of cluster ensemble and text mining for comprehensive gene expression analysis
International Journal of Bioinformatics Research and Applications
Biomedical ontology improves biomedical literature clustering performance: a comparison study
International Journal of Bioinformatics Research and Applications
The NVI clustering evaluation measure
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
How to Control Clustering Results? Flexible Clustering Aggregation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Feature selection for genomic data sets through feature clustering
International Journal of Data Mining and Bioinformatics
A polygon-based methodology for mining related spatial datasets
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
Visual decision support for ensemble clustering
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Projective clustering ensembles
Data Mining and Knowledge Discovery
How to "alternatize" a clustering algorithm
Data Mining and Knowledge Discovery
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With the development of microarray techniques, there is an increasing need of information processing methods to analyze the high throughput data. Clustering is one of the most promising candidates because of its simplicity, flexibility and robustness. However, there is no "perfect" clustering approach outperforming its counterparts, and it is hard to evaluate and combine the results from different techniques, especially in a field without much prior knowledge, such as bioinformatics. This paper proposes a meta-clustering approach to extract the information from results of different clustering techniques, so that a better interpretation of the data distribution can be obtained. A special distance measure is defined to represent the statistical "signal" of each cluster produced by various clustering techniques. The algorithm is applied on both artificial and real data Simulations show that the proposed approach is able toextract the information efficiently and accurately from the input clustering structure.