Publication Type:

Conference Paper

Source:

Advances in Intelligent Informatics, Springer International Publishing, Cham (2015)

ISBN:

9783319112183

URL:

https://link.springer.com/chapter/10.1007/978-3-319-11218-3_31

Keywords:

Vector Space Model Document Cluster Mining Model Sentence Level Relative Closeness

Abstract:

A lot of research work has been done in the area of concept mining and document similarity in past few years. But all these works were based on the statistical analysis of keywords. The major challenge in this area involves the preservation of semantics of the terms or phrases. Our paper proposes a graph model to represent the concept in the sentence level. The concept follows a triplet representation. A modified DB scan algorithm is used to cluster the extracted concepts. This cluster forms a belief network or probabilistic network. We use this network for extracting the most probable concepts in the document. In this paper we also proposes a new algorithm for document similarity. For the belief network comparison an extended chameleon Algorithm is also proposed here.

Cite this Research Publication

G. Veena and K., L. N., “An Extended Chameleon Algorithm for Document Clustering”, in Advances in Intelligent Informatics, Cham, 2015.