Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
ACM Computing Surveys (CSUR)
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Data mining: concepts and techniques
Data mining: concepts and techniques
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Self-Organizing Maps
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Adaptive Meta-Clustering Approach: Combining the Information from Different Clustering Results
CSB '02 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Gene Cluster Algorithm Based on Most Similarity Tree
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
An effective soft clustering approach to mining gene expressions from multi-source databases
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Generalized cluster aggregation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Feature selection for genomic data sets through feature clustering
International Journal of Data Mining and Bioinformatics
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Robust clustering using discriminant analysis
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Speaker diarization exploiting the eigengap criterion and cluster ensembles
IEEE Transactions on Audio, Speech, and Language Processing
Hybrid ensemble approach for classification
Applied Intelligence
Cluster ensemble in adaptive tree structured clustering
International Journal of Knowledge Engineering and Soft Data Paradigms
Integration analysis of diverse genomic data using multi-clustering results
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Optimal clustering in the context of overlapping cluster analysis
Information Sciences: an International Journal
BiETopti-BiClustering ensemble using optimization techniques
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
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Huge amount of gene expression data have been generated as a result of the human genomic project. Clustering has been used extensively in mining these gene expression data to find important genetic and biological information. Obtaining high quality clustering results is very challenging because of the inconsistency of the results of different clustering algorithms and noise in the gene expression data. Many clustering algorithms are available and different clustering algorithms may generate different clustering results due to their bias and assumptions. It is a challenging and daunting task for the genomic researchers to choose the best clustering algorithm and generate the best clustering results for their data sets. In this paper, we present a cluster ensemble framework for gene expression analysis to generate high quality and robust clustering results. In our framework, the clustering results of individual clustering algorithm are converted into a distance matrix, these distance matrices are combined and a weighted graph is constructed according to the combined matrix. Then a graph partitioning approach is used to cluster the graph to generate the final clusters. The experiment results indicate that cluster ensemble approach yields better clustering results than the single best clustering algorithm on both synthetic data set and yeast gene expression data set.