A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Pairwise Similarity for Data Clustering
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Multi-Objective Clustering Ensemble
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Performance of data resampling methods for robust class discovery based on clustering
Intelligent Data Analysis
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster Validation Using Splitting and Merging Technique
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
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In this paper a new criterion for clusters validation is proposed. Many stability measures to validate a cluster have been proposed such as Normalized Mutual Information. We propose a new criterion for clusters validation. The drawback of the common approach is discussed in this paper and then a new asymmetric criterion is proposed to assess the association between a cluster and a partition which is called Alizadeh-Parvin-Minaei criterion, APM. The APM criterion compensates the drawback of the common Normalized Mutual Information (NMI) measure. Then we employ this criterion to select the more robust clusters in the final ensemble. We also propose a new method named Extended Evidence Accumulation Clustering, EEAC, to construct the matrix of similarity from these selected clusters. Finally, we apply a hierarchical method over the obtained matrix to extract the final partition. The empirical studies show that the proposed method outperforms other ones.