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
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Kernel-based classification using quantum mechanics
Pattern Recognition
Multiorder neurons for evolutionary higher-order clustering and growth
Neural Computation
Fuzzy clustering ensemble based on mutual information
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
A consensus-driven fuzzy clustering
Pattern Recognition Letters
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
Weighted Cluster Ensemble Using a Kernel Consensus Function
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Clustering aggregation by probability accumulation
Pattern Recognition
Spectral clustering ensemble for image segmentation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
An Experimental Validation of Some Indexes of Fuzzy Clustering Similarity
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Identifying and evaluating community structure in complex networks
Pattern Recognition Letters
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
An enhanced clusterer aggregation using nebulous pool
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Nonparametric Bayesian clustering ensembles
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Combining multiple clusterings using similarity graph
Pattern Recognition
Optimized ensembles for clustering noisy data
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Soft spectral clustering ensemble applied to image segmentation
Frontiers of Computer Science in China
Hybrid ensemble approach for classification
Applied Intelligence
Tuning graded possibilistic clustering by visual stability analysis
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
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
Subspace metric ensembles for semi-supervised clustering of high dimensional data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Combining multiple clusterings via k-modes algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Clustering mixed data based on evidence accumulation
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Cluster-Based cumulative ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Clustering distributed data streams in peer-to-peer environments
Information Sciences: an International Journal
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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
Feature selection using structural similarity
Information Sciences: an International Journal
Adaptive clustering based on auto: learning algorithm
VECoS'08 Proceedings of the Second international conference on Verification and Evaluation of Computer and Communication Systems
A max metric to evaluate a cluster
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
An effective ensemble method for hierarchical clustering
Proceedings of the Fifth International C* Conference on Computer Science and Software Engineering
Knowledge augmentation via incremental clustering: new technology for effective knowledge management
International Journal of Business Information Systems
Projective clustering ensembles
Data Mining and Knowledge Discovery
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
A clustering ensemble framework based on elite selection of weighted clusters
Advances in Data Analysis and Classification
DUET: integration of dynamic and static analyses for malware clustering with cluster ensembles
Proceedings of the 29th Annual Computer Security Applications Conference
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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We explore the idea of evidence accumulation for combining the results of multiple clusterings. Initially, n d - dimensional data is decomposed into a large number of compact clusters; the K-means algorithm performs this decomposition, with several clusterings obtained by N random initializations of the K-means. Taking the co-occurrences of pairs of patterns in the same cluster as votes for their association, the data partitions are mapped into a co-association matrix of patterns. This n 脳 n matrix represents a new similarity measure between patterns. The final clusters are obtained by applying a MST-based clustering algorithm on this matrix. Results on both synthetic and real data show the ability of the method to identify arbitrary shaped clusters in multidimensional data.