A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Practical genetic algorithms
ACM Computing Surveys (CSUR)
Genetic algorithm with deterministic crossover for vector quantization
Pattern Recognition Letters
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles: a knowledge reuse framework for combining partitionings
Eighteenth national conference on Artificial intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Bagging for Path-Based Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A novel genetic algorithm for automatic clustering
Pattern Recognition Letters
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Classifier hierarchy learning by means of genetic algorithms
Pattern Recognition Letters
A new representation and operators for genetic algorithms applied to grouping problems
Evolutionary Computation
To combine steady-state genetic algorithm and ensemble learning for data clustering
Pattern Recognition Letters
An Evolutionary Approach to Clustering Ensemble
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multi-objective clustering ensemble with prior knowledge
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Integration analysis of diverse genomic data using multi-clustering results
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
A novel framework for discovering robust cluster results
DS'06 Proceedings of the 9th international conference on Discovery Science
Heterogeneous clustering ensemble method for combining different cluster results
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Exploiting the trade-off — the benefits of multiple objectives in data clustering
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Classification of textual E-mail spam using data mining techniques
Applied Computational Intelligence and Soft Computing
Projective clustering ensembles
Data Mining and Knowledge Discovery
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The clustering ensemble has emerged as a prominent method for improving robustness, stability, and accuracy of unsupervised classification solutions. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper presents a survey of genetic algorithms designed for clustering ensembles. It begins with the introduction of clustering ensembles and clustering ensemble algorithms. Subsequently, this paper describes a number of suggested genetic-guided clustering ensemble algorithms, in particular the genotypes, fitness functions, and genetic operations. Next, clustering accuracies among the genetic-guided clustering ensemble algorithms is compared. This paper concludes that using genetic algorithms in clustering ensemble improves the clustering accuracy and addresses open questions subject to future research.