Web Document Classification Using Fuzzy K-Nearest Neighbor
Aijazahamed Qazi1, R. H. Goudar2, P.S. Hiremath3

1Aijazahamed Qazi, Department of CSE, SDMCET, Dharwad, India.
2Dr. R.H.Goudar, Department of CNE, Center for PG Studies Visvesvaraya Technological University, Belgaum, India.
3Dr. P.S.Hiremath, Department of MCA, KLE Technological University, Hubli, India.

Manuscript received on 28 August 2019. | Revised Manuscript received on 21 September 2019. | Manuscript published on 30 September 2019. | PP: 471-474 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14070981119/2019©BEIESP | DOI: 10.35940/ijitee.K1407.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: With surge in the number of documents across the internet, increasing the efficiency of any retrieval model is a challenging task. As non-relevant information is retrieved across the internet, increasing the accuracy of any search model is one of the research concerns. Fuzzy classification is broadly applied to address the search issue in search engines. Fuzzy logic provides a methodology to interpret natural language using membership functions. A variant of k-Nearest Neighbor (kNN) called Fuzzy kNN is explored in this paper. This paper provides a comparative analysis of results obtained using kNN and Fuzzy kNN. The Fuzzy kNN results obtained show significant improvement.
Keywords: Term, ICF, Fuzzy kNN.
Scope of the Article: