NDLI: Different similarity measures for text classification using KNN

Content Provider	IEEE Xplore Digital Library
Author	Wajeed, M.A. Adilakshmi, T.
Copyright Year	2011
Description	Author affiliation: SCSI, Sreenidhi Institute of Science & Technology, Ghatkesar, Hyderabad, India (Wajeed, M.A.) \|\| CSE Dept., Vasavi College of Engineering, Ibrahimbagh, Hyderabad, India (Adilakshmi, T.)
Abstract	Present days humans are associated with many electronic gadgets which generate large amount of data on regular basis. The sole purpose of generated data was to meet the immediate needs and no attempt in organizing the data for later efficient retrieval was attempted. Over the period of time, the data generated became voluminous, this paper attempts to classify the huge data into different categories for easy retrieval. We have many techniques to classify the data which exists in the structured format, but not much work has been addressed when the data is available in textual form. In the present paper an attempt to classify the textual data based on its content is explored. The paper explores the process of building multi-classifier model for textual data. In the process of designing the model the K-Nearest Neighbour paradigm was employed, which has given encouraging results. The paper also attempts to explore different similarity measures, different feature selection techniques in the process of designing textual multi-classification.
Starting Page	41
Ending Page	45
File Size	805439
Page Count	5
File Format	PDF
ISBN	9781457713859
e-ISBN	9781457713866
DOI	10.1109/ICCCT.2011.6075188
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computers Squared-Euclidean Vectors Training Accuracy Text categorization Unstructured data classification Support vector machine classification Training data Euclidean Bray-Curtis Chessboard Similairty measures Manhattan
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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