Bootstrap technique in cluster analysis
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
Combining partitions by probabilistic label aggregation
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Diffusion maps-based image clustering
Proceedings of the 2006 international workshop on Research issues in digital libraries
Multi-objective clustering ensemble
International Journal of Hybrid Intelligent Systems - Hybridization of Intelligent Systems
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Optimizing correlation structure of event services considering time and capacity constraints
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Evolutionary multi-objective clustering for overlapping clusters detection
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multi-objective clustering ensemble with prior knowledge
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
Consensus clustering using spectral theory
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A multiobjective immune clustering ensemble technique applied to unsupervised SAR image segmentation
Proceedings of the ACM International Conference on Image and Video Retrieval
Clustering methods for agent distribution optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On combining multiple clusterings: an overview and a new perspective
Applied Intelligence
Hybrid ensemble approach for classification
Applied Intelligence
Analysis of vulnerability assessment results based on CAOS
Applied Soft Computing
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
Improvements in image categorization using codebook ensembles
Image and Vision Computing
Dynamic clustering using multi-objective evolutionary algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Combining multiple clusterings via k-modes algorithm
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Exploiting the trade-off — the benefits of multiple objectives in data clustering
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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
A max metric to evaluate a cluster
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
An enriched game-theoretic framework for multi-objective clustering
Applied Soft Computing
Generating multiple alternative clusterings via globally optimal subspaces
Data Mining and Knowledge Discovery
International Journal of Hybrid Intelligent Systems
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Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process. It includes detection of clusters by a set of candidate objective functions as well as their integration into the target partition. A key ingredient of the approach is a cluster goodness function that evaluates the utility of multiple clusters using re-sampling techniques. Multiobjective data clustering is obtained as a solution to a discrete optimization problem in the space of clusters. At meta-level, our algorithm incorporates conflict resolution techniques along with the natural data constraints. An empirical study on a number of artificial and real-world data sets demonstrates that multiobjective data clustering leads to valid and robust data partitions