Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Clustering gene expression patterns
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
An algorithm for clustering cDNAs for gene expression analysis
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Fuzzy Modeling for Control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
Self-Adaptive Genetic Algorithm for Clustering
Journal of Heuristics
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Evolutionary Algorithms for Clustering Gene-Expression Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Elementary Statistics Using Excel (3rd Edition)
Elementary Statistics Using Excel (3rd Edition)
A genetic algorithm for cluster analysis
Intelligent Data Analysis
Cluster Analysis
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the efficiency of evolutionary fuzzy clustering
Journal of Heuristics
A Cluster-Based Feature Selection Approach
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
An Evolutionary Algorithm for Missing Values Substitution in Classification Tasks
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data clustering using bacterial foraging optimization
Journal of Intelligent Information Systems
Chaotic ant swarm approach for data clustering
Applied Soft Computing
Automatic aspect discrimination in data clustering
Pattern Recognition
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
Dynamic clustering using combinatorial particle swarm optimization
Applied Intelligence
Relative Validity Criteria for Community Mining Algorithms
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
On the combination of relative clustering validity criteria
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Evolutionary k-means for distributed data sets
Neurocomputing
A framework to monitor clusters evolution applied to economy and finance problems
Intelligent Data Analysis
Hi-index | 0.07 |
Clustering is a useful exploratory tool for gene-expression data. Although successful applications of clustering techniques have been reported in the literature, there is no method of choice in the gene-expression analysis community. Moreover, there are only a few works that deal with the problem of automatically estimating the number of clusters in bioinformatics datasets. Most clustering methods require the number k of clusters to be either specified in advance or selected a posteriori from a set of clustering solutions over a range of k. In both cases, the user has to select the number of clusters. This paper proposes improvements to a clustering genetic algorithm that is capable of automatically discovering an optimal number of clusters and its corresponding optimal partition based upon numeric criteria. The proposed improvements are mainly designed to enhance the efficiency of the original clustering genetic algorithm, resulting in two new clustering genetic algorithms and an evolutionary algorithm for clustering (EAC). The original clustering genetic algorithm and its modified versions are evaluated in several runs using six gene-expression datasets in which the right clusters are known a priori. The results illustrate that all the proposed algorithms perform well in gene-expression data, although statistical comparisons in terms of the computational efficiency of each algorithm point out that EAC outperforms the others. Statistical evidence also shows that EAC is able to outperform a traditional method based on multiple runs of k-means over a range of k.