In this thesis study  a new approach to automatic word categorization
which improves both the efficiency of the algorithm and the quality
of the formed clusters is presented. The unigram and the bigram statistics
of a corpus of about two million words are used with an efficient distance
function to measure the similarities of words, and a greedy algorithm to
put the words into  clusters. The notions of fuzzy clustering like cluster
prototypes, degree of membership are used to form up the clusters.
Different distance metrics are analyzed using the algorithm. Empirical
comparisons are made in order to support the  discussions proposed
for the type of distance metric that would be most suitable for measuring
the similarity between linguistic elements.  The algorithm is of
unsupervised type and the number of clusters are determined at run-time.