On Clustering Validation Techniques
Journal of Intelligent Information Systems
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: Hybrid learning machines
Neurocomputing
Visualizing asymmetric proximities with SOM and MDS models
Neurocomputing
Support vector machine classifiers for asymmetric proximities
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Editorial: Hybrid intelligent algorithms and applications
Information Sciences: an International Journal
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
On the Performance of Clustering in Hilbert Spaces
IEEE Transactions on Information Theory
Asymmetric clustering using the alpha-beta divergence
Pattern Recognition
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In this paper, an asymmetric k-means clustering algorithm is presented. The asymmetric version of this algorithm is derived using the asymmetric coefficients, which convey the information provided by the asymmetry in analyzed data sets. The formulation of the asymmetric k-means algorithm is motivated by the fact that, when an analyzed data set has the asymmetric nature, a data analysis algorithm should properly adjust to this nature. The traditional k-means approach using the symmetric dissimilarities does not apply correctly to this kind of phenomenon in data. We propose the k-means algorithm using the asymmetric coefficients, which has the ability to reflect the asymmetric relationships between objects in analyzed data sets. The results of our experimental study on real data show that the asymmetric k-means approach outperforms its symmetric counterpart.