A survey on semantic document clustering

Abstract

Clustering is the process of partitioning a set of data objects into subsets. It is commonly used technique in data mining, information retrieval, and knowledge discovery for finding hidden patterns or objects from a data of different category. Text clustering process deals with grouping of an unstructured collection of documents into semantically related groups. A document is considered as a bag of words in traditional document clustering methods; however, semantic meaning of word is not considered. Thus, more informative features like concept weight are important to achieve accurate document clustering and this can be achieved through semantic document clustering because it takes meaningful relationship into account. This paper highlights major challenges in traditional document clustering and semantic document clustering along with brief discussion. This paper identifies five major areas under semantic clustering and presents a survey of 17 papers that has studied, covering major significant works. Moreover, this paper also provides a survey of tools, ontology databases, and algorithms, which help in applying and evaluating document clustering. The presented survey is used in preparing the proposed work in the same direction. This proposed work uses the concept weight for text clustering system which is to be developed based on a Hierarchical Agglomerative Clustering, Bisecting k-means algorithm, and Self Organized Map Neural Network in accordance with the principles of WordNet ontology as a background knowledge.