Paper Title
A Distributed K-Nearest-Neighbor Algorithm For Text Categorization
Article Identifiers
: Authors
Keywords
KNN;Recall; F-measure; Precision
Abstract
Text categorization is the application of text mining. Content classification is a supervised learning technique, it plays important role for indexing of document like different applications. Content order has abundant applications, in several fields and for different sorts of information. Numerous issues identified with information stockpiling, administration and recovery can be defined as far as content order. Clustering plays vital part in text mining. K-means clustering is widely used text categorization technique, still more work can be carried out to improve the performance of k-means text classification technique. In this paper we have proposed parallelization of the renowned k-means clustering algorithm. The parallel implementation of k-means uses data parallelism. In this paper we have compared the performance of parallel k-means text classification with sequential k-means with respect to time factor i.e. total time required for content classification and eventually we have calculated the F-measure value by calculating precision and recall which decides what percentage of messages were classified correctly.
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How To Cite (APA)
SUMAN SAHU & DR.ABHA CHOUBEY (September-2017). A Distributed K-Nearest-Neighbor Algorithm For Text Categorization. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 2(9), 10-13. https://ijnrd.org/papers/IJNRD1709004.pdf
Issue
Volume 2 Issue 9, September-2017
Pages : 10-13
Other Publication Details
Paper Reg. ID: IJNRD_170133
Published Paper Id: IJNRD1709004
Downloads: 000122253
Research Area: Engineering
Author Type: Indian Author
Country: DURG, CHHATTISGARH, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD1709004.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD1709004
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