22Apr 2017

CLASSIFICATION OF WEB DOCUMENTS USING HYBRID FEATURE SELECTION.

  • Research Scholar, Periyar E.V.R College (Autonomous), Trichy.
  • Assistant Professor, Periyar E.V.R. College (Autonomous), Trichy
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Knowledge discovery and data mining is a process of retrieving the meaningful knowledge from the raw data, using different techniques. Therefore, text mining is a sub domain of knowledge discovery from the text data. Web mining is a one class of data mining. Web Mining is a variation of data mining that distills untapped source of abundantly available free textual information. The need and importance of web mining is growing along with the massive volumes of data generated in web day-to-day life. Feature selection is an effective technique for dimension reduction and an essential step in successful data mining applications. It is a research area of great practical significance and has been developed and evolved to answer the challenges due to data of increasingly high dimensionality. In this paper, a hybrid feature selection is proposed. The Relative Reduct and Particle Swarm Optimization Technique are hybridized to reduce the size of the feature space

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[V. David Martin and T. N. Ravi. (2017); CLASSIFICATION OF WEB DOCUMENTS USING HYBRID FEATURE SELECTION. Int. J. of Adv. Res. 5 (Apr). 174-181] (ISSN 2320-5407). www.journalijar.com

V David Martin
Research Scholar, Department of Computer Science, Periyar E.V.R. College (Autonomous)

DOI:

Article DOI: 10.21474/IJAR01/3793      
DOI URL: http://dx.doi.org/10.21474/IJAR01/3793

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