SNN: A Supervised Clustering Algorithm
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Supervised clustering: algorithms and applications
Supervised clustering: algorithms and applications
Incremental Figure-Ground Segmentation Using Localized Adaptive Metrics in LVQ
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
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In this paper, a prototype-based supervised clustering algorithm is proposed. The proposed algorithm, called the Supervised Growing Neural Gas algorithm (SGNG), incorporates several techniques from some unsupervised GNG algorithms such as the adaptive learning rates and the cluster repulsion mechanisms of the Robust Growing Neural Gas algorithm, and the Type Two Learning Vector Quantization (LVQ2) technique. Furthermore, a new prototype insertion mechanism and a clustering validity index are proposed. These techniques are designed to utilize class labels of the training data to guide the clustering. The SGNG algorithm is capable of clustering adjacent regions of data objects labeled with different classes, formulating topological relationships among prototypes and automatically determining the optimal number of clusters using the proposed validity index. To evaluate the effectiveness of the SGNG algorithm, two experiments are conducted. The first experiment uses two synthetic data sets to graphically illustrate the potential with respect to growing ability, ability to cluster adjacent regions of different classes, and ability to determine the optimal number of prototypes. The second experiment evaluates the effectiveness using the UCI benchmark data sets. The results from the second experiment show that the SGNG algorithm performs better than other supervised clustering algorithms for both cluster impurities and total running times.