Density Based Self Organizing Incremental Neural Network for data stream clustering

Abstract

Clustering is an important technique widely used in many areas such as machine learning, pattern recognition, data analysis etc. Data stream clustering is a branch of clustering that draws much attention in recent years, where data objects are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network(DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining on-line unsupervised learning and topology learning by means of competitive Hebbian learning rule [19]. By adopting a density-based clustering mechanism, DenSOINN can discover arbitrarily shaped clusters and diminish the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance equally on both raw data and normalized data.