Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Reinterpreting the Category Utility Function
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Voting-Merging: An Ensemble Method for Clustering
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
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
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Non-redundant clustering with conditional ensembles
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Combining Multiple Clusterings by Soft Correspondence
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering ensemble based on mutual information
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Meta methods for model sharing in personal information systems
ACM Transactions on Information Systems (TOIS)
Ensemble clustering with voting active clusters
Pattern Recognition Letters
A consensus based approach to constrained clustering of software requirements
Proceedings of the 17th ACM conference on Information and knowledge management
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
A new method for hierarchical clustering combination
Intelligent Data Analysis
A scalable framework for cluster ensembles
Pattern Recognition
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
How to Control Clustering Results? Flexible Clustering Aggregation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
From comparing clusterings to combining clusterings
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Computation of initial modes for K-modes clustering algorithm using evidence accumulation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Adaptive cluster ensemble selection
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Distributed data mining and agents
Engineering Applications of Artificial Intelligence
Self-organization in wireless networks using the autonomic behavior
GIIS'09 Proceedings of the Second international conference on Global Information Infrastructure Symposium
On voting-based consensus of cluster ensembles
Pattern Recognition
A novel hierarchical-clustering-combination scheme based on fuzzy-similarity relations
IEEE Transactions on Fuzzy Systems
A new efficient approach in clustering ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
The multi-view information bottleneck clustering
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Weighted partition consensus via kernels
Pattern Recognition
Robust clustering using discriminant analysis
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
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
The effect of cooling functions on ensemble clustering using simulated annealing
Intelligent Data Analysis
Clustering of Adolescent Criminal Offenders using Psychological and Criminological Profiles
Proceedings of the 2010 conference on Data Mining for Business Applications
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
A review: accuracy optimization in clustering ensembles using genetic algorithms
Artificial Intelligence Review
Comparing clustering and metaclustering algorithms
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Combining multiple clusterings using fast simulated annealing
Pattern Recognition Letters
Subspace metric ensembles for semi-supervised clustering of high dimensional data
ECML'06 Proceedings of the 17th European conference on Machine Learning
A comparison of three graph partitioning based methods for consensus clustering
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Joint cluster based co-clustering for clustering ensembles
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Combining multiple clusterings via k-modes algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
From cluster ensemble to structure ensemble
Information Sciences: an International Journal
Community detection via heterogeneous interaction analysis
Data Mining and Knowledge Discovery
Adaptive clustering based on auto: learning algorithm
VECoS'08 Proceedings of the Second international conference on Verification and Evaluation of Computer and Communication Systems
An application of the self-organizing map to multiple view unsupervised learning
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Detecting pedestrian flocks by fusion of multi-modal sensors in mobile phones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
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
Data weighing mechanisms for clustering ensembles
Computers and Electrical Engineering
BiETopti-BiClustering ensemble using optimization techniques
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Multistage approach for clustering and classification of ECG data
Computer Methods and Programs in Biomedicine
A theoretic framework of K-means-based consensus clustering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Pairwise similarity for cluster ensemble problem: link-based and approximate approaches
Transactions on Large-Scale Data- and Knowledge-centered systems IX
An ensemble-clustering-based distance metric and its applications
International Journal of Business Intelligence and Data Mining
Effects of resampling method and adaptation on clustering ensemble efficacy
Artificial Intelligence Review
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A data set can be clustered in many ways dependingon the clustering algorithm employed, parameter settingsused and other factors. Can multiple clusterings becombined so that the final partitioning of data providesbetter clustering? The answer depends on the quality ofclusterings to be combined as well as the properties of thefusion method. First, we introduce a unifiedrepresentation for multiple clusterings and formulate thecorresponding categorical clustering problem. As aresult, we show that the consensus function is related tothe classical intra-class variance criterion using thegeneralized mutual information definition. Second, weshow the efficacy of combining partitions generated byweak clustering algorithms that use data projections andrandom data splits. A simple explanatory model is offeredfor the behavior of combinations of such weak clusteringcomponents. We analyze the combination accuracy as afunction of parameters controlling the power andresolution of component partitions as well as the learningdynamics vs. the number of clusterings involved. Finally,some empirical studies compare the effectiveness ofseveral consensus functions.