A stochastic self-organizing map for proximity data
Neural Computation
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps and clustering methods for matrix data
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Median Topographic Maps for Biomedical Data Sets
Similarity-Based Clustering
Performance improvements of a Kohonen self organizing classification algorithm on sparse data sets
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
Career-Path Analysis Using Optimal Matching and Self-Organizing Maps
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Speeding up the dissimilarity self-organizing maps by branch and bound
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
A new adaptation of self-organizing map for dissimilarity data
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Self-organizing map for symbolic data
Fuzzy Sets and Systems
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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In many real-world applications, data cannot be accurately represented by vectors. In those situations, one possible solution is to rely on dissimilarity measures that enable a sensible comparison between observations.Kohonen's self-organizing map (SOM) has been adapted to data described only through their dissimilarity matrix. This algorithm provides both nonlinear projection and clustering of nonvector data. Unfortunately, the algorithm suffers from a high cost that makes it quite difficult to use with voluminous data sets. In this paper, we propose a new algorithm that provides an important reduction in the theoretical cost of the dissimilarity SOM without changing its outcome (the results are exactly the same as those obtained with the original algorithm). Moreover, we introduce implementation methods that result in very short running times.Improvements deduced from the theoretical cost model are validated on simulated and real-world data (a word list clustering problem). We also demonstrate that the proposed implementation methods reduce the running time of the fast algorithm by a factor up to three over a standard implementation.