A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
An empirical comparison of four initialization methods for the K-Means algorithm
Pattern Recognition Letters
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
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
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
The effect of cooling functions on ensemble clustering using simulated annealing
Intelligent Data Analysis
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
A clustering-ensemble approach based on voting
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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Previous clustering ensemble algorithms usually use a consensus function to obtain a final partition from the outputs of the initial clustering. In this paper, we propose a new clustering ensemble method, which generates a new feature space from initial clustering outputs. Multiple runs of an initial clustering algorithm like k-means generate a new feature space, which is significantly better than pure or normalized feature space. Therefore, running a simple clustering algorithm on generated feature space can obtain the final partition significantly better than pure data. In this method, we use a modification of k-means for initial clustering runs named as "Intelligent k-means", which is especially defined for clustering ensembles. The results of the proposed method are presented using both simple k-means and intelligent kmeans. Fast convergence and appropriate behavior are the most interesting points of the proposed method. Experimental results on real data sets show effectiveness of the proposed method.