Investigation of machine learning tools for document clustering and classification

Abstract

Data clustering is a problem of discovering the underlying data structure without any prior information about the data. The focus of this thesis is to evaluate a few of the modern clustering algorithms in order to determine their performance in adverse conditions. Synthetic Data Generation software is presented as a useful tool both for generating test data and for investigating results of the data clustering. Several theoretical models and their behavior are discussed, and, as the result of analysis of a large number of quantitative tests, we come up with a set of heuristics that describe the quality of clustering output in different adverse conditions.