Algorithms for clustering data
Algorithms for clustering data
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
A Multi-SVM Classification System
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
A clustering method based on boosting
Pattern Recognition Letters
Maximum likelihood combination of multiple clusterings
Pattern Recognition Letters
Intelligent Data Analysis
CONSENSUS-BASED ENSEMBLES OF SOFT CLUSTERINGS
Applied Artificial Intelligence
An adaptable Gaussian neuro-fuzzy classifier
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A novel clustering algorithm based on gravity and cluster merging
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A hierarchical clusterer ensemble method based on boosting theory
Knowledge-Based Systems
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A multi-clustering fusion method is presented based on combining several runs of a clustering algorithm resulting in a common partition. More specifically, the results of several independent runs of the same clustering algorithm are appropriately combined to obtain a partition of the data which is not affected by initialization and overcomes the instabilities of clustering methods. Finally, the fusion procedure starts with the clusters produced by the combining part and finds the optimal number of clusters in the data set according to some predefined criteria. The unsupervised multi-clustering method implemented in this work is quite general. There is ample room for the implementation and testing with any existing clustering algorithm that has unstable results. Experiments using both simulated and real data sets indicate that the multi-clustering fusion algorithm is able to partition a set of data points to the optimal number of clusters not constrained to be hyperspherically shaped.