Self Organizing Maps for Text Clustering

Neural Networks are analytic techniques modeled after the (hypothesized) processes of learning in the cognitive system and the neurological functions of the brain and capable of predicting new observations (on specific variables) from other observations (on the same or other variables) after executing a process of so-called learning from existing data. Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. It is a natural proof that some problems that are beyond the scope of current computers are indeed solvable by small energy efficient packages. This brain modeling also promises a technical way to develop machine solutions. In this paper we highlight the efficient use of Self organizing maps for text clustering. Self-Organizing Maps (SOM) are unsupervised Artificial Neural Networks (ANN) which are mathematically characterized by transforming high-dimensional data into twodimension representation, enabling automatic clustering of the input, while preserving higher order topology. In this paper we analyze how the SOM and Growing Hierarchical SOM architectures efficiently cluster data. Document clustering is the act of collecting similar documents into bins, where similarity is some function on a document. Clustering analysis allows one to group together clients or data items that have similar characteristics.