Large-Scale Parallel Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Robust Competitive Clustering Algorithm With Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
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
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A new N-gram feature extraction-selection method for malicious code
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Localizing program logical errors using extraction of knowledge from invariants
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
A novel classifier ensemble method based on class weightening in huge dataset
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
An innovative feature selection using fuzzy entropy
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
A new clustering algorithm with the convergence proof
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
On possibility of conditional invariant detection
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Linkage learning based on local optima
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Detection of cancer patients using an innovative method for learning at imbalanced datasets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A metric to evaluate a cluster by eliminating effect of complement cluster
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
A new asymmetric criterion for cluster validation
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Conceptual clustering in information retrieval
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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It has been proved that ensemble learning is a solid approach to reach more accurate, stable, robust, and novel results in all data mining tasks such as clustering, classification, regression and etc. Clustering ensemble as a sub-field of ensemble learning is a general approach to improve the performance of clustering task. In this paper by defining a new criterion for clusters validation named Modified Normalized Mutual Information (MNMI), a clustering ensemble framework is proposed. In the framework first a large number of clusters are prepared and then some of them are selected for the final ensemble. The clusters which satisfy a threshold of the proposed metric are selected to participate in final clustering ensemble. For combining the chosen clusters, a co-association based consensus function is applied. Since the Evidence Accumulation Clustering (EAC) method can't derive the co-association matrix from a subset of clusters, Extended Evidence Accumulation Clustering (EEAC), is applied for constructing the co-association matrix from the subset of clusters. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard datasets. The empirical studies show promising results for the ensemble obtained using the proposed criterion comparing with the ensemble obtained using the standard clusters validation criterion.