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Advances in Intelligent Informatics pp 335–348Cite as

An Extended Chameleon Algorithm for Document Clustering

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 320))

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.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science and Application, Amrita Vishwa Vidyapeetham, Amritapuri, India

    G. Veena & N. K. Lekha

Authors
  1. G. Veena
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  2. N. K. Lekha
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Corresponding author

Correspondence to G. Veena .

Editor information

Editors and Affiliations

  1. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

  2. Indian Institute of Information Technology and Management- - Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

  3. Faculty of Design, Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  4. Department of Information Systems and Operations Management, University of Florida, Warrington College of Business, Florida, Florida, USA

    Selwyn Piramuthu

  5. University of Applied Sciences, Institute for Information Systems, Olten, Switzerland

    Thomas Hanne

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© 2015 Springer International Publishing Switzerland

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Veena, G., Lekha, N.K. (2015). An Extended Chameleon Algorithm for Document Clustering. In: El-Alfy, ES., Thampi, S., Takagi, H., Piramuthu, S., Hanne, T. (eds) Advances in Intelligent Informatics. Advances in Intelligent Systems and Computing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-11218-3_31

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  • DOI: https://doi.org/10.1007/978-3-319-11218-3_31

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11217-6

  • Online ISBN: 978-3-319-11218-3

  • eBook Packages: EngineeringEngineering (R0)

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