Background:
Considering the increasing volume of text document information on Internet
pages, dealing with such a tremendous amount of knowledge becomes totally complex due to
its large size. Text clustering is a common optimization problem used to manage a large amount of
text information into a subset of comparable and coherent clusters.
Aims:
This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the
problem of the text document clustering through modeling the β-hill climbing technique for partitioning
the similar documents into the same cluster.
Methods:
The β parameter is the primary innovation in β-hill climbing technique. It has been introduced
in order to perform a balance between local and global search. Local search methods are
successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean
techniques.
Results:
Experiments were conducted on eight benchmark standard text datasets with different
characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results
proved that the proposed β-hill climbing achieved better results in comparison with the original hill
climbing technique in solving the text clustering problem.
Conclusion:
The performance of the text clustering is useful by adding the β operator to the hill
climbing.
Document Clustering Based on Semi-Supervised Term Clustering
