Algorithms for clustering data
Algorithms for clustering data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Approaches to Gaussian Mixture Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-line hierarchical clustering
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Multi-clustering Fusion Algorithm
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Definition of MV load diagrams via weighted evidence accumulation clustering using subsampling
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
Definition of MV load diagrams via weighted evidence accumulation clustering using subsampling
ISPRA'07 Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation
One Lead ECG Based Personal Identification with Feature Subspace Ensembles
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
ECML '07 Proceedings of the 18th European conference on Machine Learning
CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS
Applied Artificial Intelligence
Semi-supervised Classification Based on Clustering Ensembles
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Robust clustering using discriminant analysis
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Speaker diarization exploiting the eigengap criterion and cluster ensembles
IEEE Transactions on Audio, Speech, and Language Processing
Soft spectral clustering ensemble applied to image segmentation
Frontiers of Computer Science in China
Hybrid ensemble approach for classification
Applied Intelligence
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
Bagging-based spectral clustering ensemble selection
Pattern Recognition Letters
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
High-order co-clustering text data on semantics-based representation model
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
A clustering-ensemble approach based on voting
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
A generative dyadic aspect model for evidence accumulation clustering
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
A study of embedding methods under the evidence accumulation framework
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Scene image clustering based on boosting and GMM
Proceedings of the Second Symposium on Information and Communication Technology
Combining multiple clusterings via k-modes algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Semi-supervised multiple classifier systems: background and research directions
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Projective clustering ensembles
Data Mining and Knowledge Discovery
Information Sciences: an International Journal
Adaptive evidence accumulation clustering using the confidence of the objects' assignments
PAKDD'12 Proceedings of the 2012 Pacific-Asia conference on Emerging Trends in Knowledge Discovery and Data Mining
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
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
BiETopti-BiClustering ensemble using optimization techniques
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Pairwise similarity for cluster ensemble problem: link-based and approximate approaches
Transactions on Large-Scale Data- and Knowledge-centered systems IX
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Given an arbitrary data set, to which no particular parametrical, statistical or geometrical structure can be assumed, different clustering algorithms will in general produce different data partitions. In fact, several partitions can also be obtained by using a single clustering algorithm due to dependencies on initialization or the selection of the value of some design parameter. This paper addresses the problem of finding consistent clusters in data partitions, proposing the analysis of the most common associations performed in a majority voting scheme. Combination of clustering results are performed by transforming data partitions into a co-association sample matrix, which maps coherent associations. This matrix is then used to extract the underlying consistent clusters. The proposed methodology is evaluated in the context of k-means clustering, a new clustering algorithm - voting-k-means, being presented. Examples, using both simulated and real data, show how this majority voting combination scheme simultaneously handles the problems of selecting the number of clusters, and dependency on initialization. Furthermore, resulting clusters are not constrained to be hyperspherically shaped.