Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Ensembles of Partitions via Data Resampling
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
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
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Exploiting context analysis for combining multiple entity resolution systems
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Collaborative clustering with background knowledge
Data & Knowledge Engineering
International Journal of Hybrid Intelligent Systems - Hybrid Fuzzy Models
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
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Cluster ensembles are deemed to be better than single clustering algorithms for discovering complex or noisy structures in data. Various heuristics for constructing such ensembles have been examined in the literature, e.g., random feature selection, weak clusterers, random projections, etc. Typically, one heuristic is picked at a time to construct the ensemble. To increase diversity of the ensemble, several heuristics may be applied together. However, not any combination may be beneficial. Here we apply a standard genetic algorithm (GA) to select from 7 standard heuristics for k-means cluster ensembles. The ensemble size is also encoded in the chromosome. In this way the data is forced to guide the selection of heuristics as well as the ensemble size. Eighteen moderate-size datasets were used: 4 artificial and 14 real. The results resonate with our previous findings in that high diversity is not necessarily a prerequisite for high accuracy of the ensemble. No particular combination of heuristics appeared to be consistently chosen across all datasets, which justifies the existing variety of cluster ensembles. Among the most often selected heuristics were random feature extraction, random feature selection and random number of clusters assigned for each ensemble member. Based on the experiments, we recommend that the current practice of using one or two heuristics for building k-means cluster ensembles should be revised in favour of using 3-5 heuristics.