Algorithms for clustering data
Algorithms for clustering data
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Improved Combinatorial Algorithms for the Facility Location and k-Median Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
On Learning Asymmetric Dissimilarity Measures
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Asymmetric Agglomerative Hierarchical Clustering Algorithms and Their Evaluations
Journal of Classification
A toolbox for K-centroids cluster analysis
Computational Statistics & Data Analysis
Aggregation of asymmetric distances in Computer Science
Information Sciences: an International Journal
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
An experimental study on asymmetric self-organizing map
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
k-Means clustering of asymmetric data
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Optimal adaptive k-means algorithm with dynamic adjustment of learning rate
IEEE Transactions on Neural Networks
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We propose the use of an asymmetric dissimilarity measure in centroid-based clustering. The dissimilarity employed is the Alpha-Beta divergence (AB-divergence), which can be asymmetrized using its parameters. We compute the degree of asymmetry of the AB-divergence on the basis of the within-cluster variances. In this way, the proposed approach is able to flexibly model even clusters with significantly different variances. Consequently, this method overcomes one of the major drawbacks of the standard symmetric centroid-based clustering.