Textual Document Clustering in Traditional and Modern Approaches (Review)

Abstract

An enormous quantity of textual documents is created from the advanced technological use concerning describing, intelligence, interconnection, and thousands of distinct authorizations and was expanding each moment of quotidian circumstances. The Clustering is an automated established process to organize the database on features. Outwardly implementing a clustering technique to textual data, a huge quantity of unstructured data is losing the capability of sharing knowledge. There are many tools and techniques proposed. This paper present and categorized the textual document clustering algorithms (approaches) into two types are classical and modern approaches. Both approaches are implemented to those textual data to obtain and consolidate knowledge from discharged to an extraordinary impression of a prepared document. The two important factors in clustering process are speed of clustering process and accuracy or purity of data clusters. This review paper can be benefits to many researchers who concern on textual document clustering, text mining and data scientist.