Publication Type : Conference Proceedings
Publisher : Advances in Intelligent Informatics, Springer International Publishing,
Source : Advances in Intelligent Informatics, Springer International Publishing, Volume 320, Cham, p.335-348 (2015)
Url : https://link.springer.com/chapter/10.1007/978-3-319-11218-3_31
ISBN : 9783319112183
Keywords : Vector Space Model Document Cluster Mining Model Sentence Level Relative Closeness
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Computational Linguistics and Indic Studies
Department : Computer Science
Verified : Yes
Year : 2015
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