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An Online Semi-Supervised Clustering Algorithm Based on a Self-organizing Incremental Neural Network

This paper presents an online semi-supervised clustering algorithm based on a self-organizing incremental neural network (SOINN). Using labeled data and a large amount of unlabeled data, the proposed semi-supervised SOINN (ssSOINN) can automatically learn the topology of input data distribution without any prior knowledge such as the number of nodes or a good network structure; it can subsequently divide the structure into sub-structures as the need arises. Experimental results we obtained for artificial data and real-world data show that the ssSOINN has superior performance for separating data distributions with high-density overlap and that ssSOINN Classifier (S3C) is an efficient classifier.