Associative Clustering for Clusters of Arbitrary Distribution Shapes
Neural Processing Letters
Using Competitive Learning in Neural Networks for Cluster-Detection-and-Labeling
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
A novel similarity measure for data clustering
Intelligent Data Analysis
Semi-supervised classification method for dynamic applications
Fuzzy Sets and Systems
A new approach to clustering data with arbitrary shapes
Pattern Recognition
Auto-adaptive and dynamical clustering neural network
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
A new kernel-based algorithm for online clustering
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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We propose an unsupervised neural net which clusters a set of experimental data according to a given generic interpoint similarity measure, and then assigns to each new input its appropriate cluster label. The network can do this for clusters of any shape, and without knowing in advance the number of clusters to be created. We call this two-layer net a cluster-detection-and-labeling (CDL) net. In it, the concept of similarity and closeness with regard to distance are combined. Specifically, clusters are represented by a set of prototypes, and the similarities between an input vector and these prototypes are calculated as inner products of these vectors compared to some thresholds. These thresholds, which depend on the distance between the input vector and the prototype, are calculated in a separate threshold calculating unit. The data are cycled through the network several times. At the end of each cycle the clusters are evaluated, and only those with more than a specified number of samples are retained. The others are fed back through the updated network. This process terminates according to a suitable criterion, such as when a prespecified portion of the data are classified. The performance of the CDL network has been compared with that of the winner-take-all (WTA) network for several different cluster structures, since the latter is widely used in cluster analysis applications. These studies demonstrate that the new network performs well for all the tested cluster shapes, also for those cases where the WTA network completely fails