Algorithms for clustering data
Algorithms for clustering data
Elements of information theory
Elements of information theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Reinterpreting the Category Utility Function
Machine Learning
Machine Learning
Evidence Accumulation Clustering Based on the K-Means Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Information-theoretical methods in clustering
Information-theoretical methods in clustering
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A divisive information theoretic feature clustering algorithm for text classification
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
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Axiomatic Concensus Theory in Group Choice and Biomathematics (Frontiers in Applied Mathematics, 29)
Axiomatic Concensus Theory in Group Choice and Biomathematics (Frontiers in Applied Mathematics, 29)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
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
Analysis of Consensus Partition in Cluster Ensemble
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding natural clusters using multi-clusterer combiner based on shared nearest neighbors
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Cluster-Based cumulative ensembles
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
Edge Detection from Global and Local Views Using an Ensemble of Multiple Edge Detectors
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Enhanced bisecting k-means clustering using intermediate cooperation
Pattern Recognition
An Evidence Accumulation Approach to Constrained Clustering Combination
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Fragment-based clustering ensembles
Proceedings of the 18th ACM conference on Information and knowledge management
Collaborative clustering with background knowledge
Data & Knowledge Engineering
On voting-based consensus of cluster ensembles
Pattern Recognition
Pattern Recognition
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
Estimation of the number of clusters using heterogeneous multiple classifier system
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Comparing clustering and metaclustering algorithms
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
A generative dyadic aspect model for evidence accumulation clustering
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
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
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
Generalized Adjusted Rand Indices for cluster ensembles
Pattern Recognition
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
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
A max metric to evaluate a cluster
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Wiley Interdisciplinary Reviews: 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
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
Cluster ensemble selection based on relative validity indexes
Data Mining and Knowledge Discovery
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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Over the past few years, there has been a renewed interest in the consensus clustering problem. Several new methods have been proposed for finding a consensus partition for a set of n data objects that optimally summarizes an ensemble. In this paper, we propose new consensus clustering algorithms with linear computational complexity in n. We consider clusterings generated with random number of clusters, which we describe by categorical random variables. We introduce the idea of cumulative voting as a solution for the problem of cluster label alignment, where, unlike the common one-to-one voting scheme, a probabilistic mapping is computed. We seek a first summary of the ensemble that minimizes the average squared distance between the mapped partitions and the optimal representation of the ensemble, where the selection criterion of the reference clustering is defined based on maximizing the information content as measured by the entropy. We describe cumulative vote weighting schemes and corresponding algorithms to compute an empirical probability distribution summarizing the ensemble. Given the arbitrary number of clusters of the input partitions, we formulate the problem of extracting the optimal consensus as that of finding a compressed summary of the estimated distribution that preserves maximum relevant information. An efficient solution is obtained using an agglomerative algorithm that minimizes the average generalized Jensen-Shannon divergence within the cluster. The empirical study demonstrates significant gains in accuracy and superior performance compared to several recent consensus clustering algorithms.