Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Machine Learning
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
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
Clustering by Chaotic Neural Networks with Mean Field Calculated Via Delaunay Triangulation
HAIS '08 Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems
Belief Functions and Cluster Ensembles
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Fragmentary Synchronization in Chaotic Neural Network and Data Mining
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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
Selecting diversifying heuristics for cluster ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Ensemble clustering in the belief functions framework
International Journal of Approximate Reasoning
Clustering of Adolescent Criminal Offenders using Psychological and Criminological Profiles
Proceedings of the 2010 conference on Data Mining for Business Applications
Advancing data clustering via projective clustering ensembles
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
A quantitative comparison of functional MRI cluster analysis
Artificial Intelligence in Medicine
Integrating community matching and outlier detection for mining evolutionary community outliers
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Projective clustering ensembles
Data Mining and Knowledge Discovery
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
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In this paper we propose an unsupervised voting-merging scheme that is capable of clustering data sets, and also of finding the number of clusters existing in them. The voting part of the algorithm allows us to combine several runs of clustering algorithms resulting in a common partition. This helps us to overcome instabilities of the clustering algorithms and to improve the ability to find structures in a data set. Moreover, we develop a strategy to understand, analyze and interpret these results. In the second part of the scheme, a merging procedure starts on the clusters resulting by voting, in order to find the number of clusters in the data set.