Clustering and Relating Research Papers using Self-Organizing Maps

Abstract

A Self-Organizing Map (SOM) is a powerful tool for data analysis, clustering, and dimensionality reduction. It is an unsupervised artificial neural network that maps a set of n-dimensional vectors to a two-dimensional topographic map. Being unsupervised, SOMs need little input to be successfully deployed. The only inputs needed by a SOM are its own parameters such as its size, number of iterations, and its initial learning rate. The quality and accuracy of the solution offered by a SOM depend on choosing the right values for such parameters. Different attempts have been made to use the genetic algorithm to optimize these parameters for random inputs or for specific applications such as the traveling salesman problem. To the best knowledge of the authors, no roadmaps for selecting these parameters were presented in the literature. In this thesis, we present the first results of a proposed roadmap for optimizing these parameters using the genetic algorithm and we show its effectiveness by applying it on the classical color clustering problem as a case study. With the huge amount of published research papers, retrieving relevant information is a difficult task for any researcher. Effective clustering algorithms can help improve and simplify the retrieval process. After testing our proposed approach on the case study, we applied our proposed approach on automatic clustering of text documents. The proposed method is applied to cluster 3 scientific papers datasets using their keywords. Similar research papers were mapped closer to each other. This thesis is divided into 7 Chapters as follows: chapter 1 is an introduction to the research in this thesis. Chapter 2 discusses document clustering. It defines document clustering highlighting the difference