A survey of kernel and spectral methods for clustering
Pattern Recognition
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Median Topographic Maps for Biomedical Data Sets
Similarity-Based Clustering
Neural gas clustering for dissimilarity data with continuous prototypes
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Topographic mapping of large dissimilarity data sets
Neural Computation
Approximate kernel k-means: solution to large scale kernel clustering
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
How to quantitatively compare data dissimilarities for unsupervised machine learning?
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Kernel robust soft learning vector quantization
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Kernel fuzzy c-means with automatic variable weighting
Fuzzy Sets and Systems
Learning vector quantization for (dis-)similarities
Neurocomputing
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We present a Kernel Neural Gas (KNG) algorithm, to generalize the original Neural Gas (NG) algorithm into a higher dimensional feature space. The proposed KNG algorithm can successfully tackle nonlinearly structured datasets. Compared with several existing kernel clustering algorithms, the KNG can be insensitive to initializations due to employing the sequential learning strategy and the neighborhood cooperation scheme. Further, a Distortion Sensitive KNG (DSKNG) algorithm is proposed to tackle the imbalanced clustering problem. Experimental results show that our KNG algorithm can successfully deal with nonlinearly structured datasets and multi-modal datasets, while the imbalanced clusters are detected by the DSKNG.