Abstract:
Clustering is one of the important data mining tasks. It is the process of partitioning dataset into
clusters so that similar objects are placed into the same cluster while dissimilar objects are placed into
different clusters. Many clustering techniques have been developed and studied. Based on different
theories and methodologies, their types include partitional, hierarchical, density-based, grid-based and
model-based clustering algorithms.
A partitional clustering algorithm Fuzzy c-means is a well-known and widely used algorithm for data
clustering. Fuzzy c-means uses a fuzzy membership matrix U to represent the degree of membership
for data points with the clusters. In this thesis, a new step is introduced to Fuzzy c-means clustering
algorithm. In this new step, the fuzzy membership matrix is biased through a suggested multiplying
factor to improve its accuracy. Experimental results on both synthetic and real data have shown the
better performance attained by improved Fuzzy c-means in comparison to classical Fuzzy c-means
algorithm.
