Hybrid SVD Model for Document Representation
P. Kalpana1, R. Kirubakaran2, P. Tamije Selvy3
1P. Kalpana, Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Technology, Coimbatore (Tamil Nadu), India.
2R. Kirubakaran, Assistant Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
3Dr. P. Tamije Selvy, Professor, Department of Computing Science and Engineering, Sri Krishna College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 28 September 2019 | Revised Manuscript received on 10 November 2019 | Manuscript Published on 22 November 2019 | PP: 1147-1150 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11910986S319/19©BEIESP | DOI: 10.35940/ijeat.F1191.0986S319
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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: Document clusters are the way to segment a certain set of text into racial groups. Nowadays all records are in electronic form due to the problem of retrieving appropriate document from the big database. The objective is to convert text consisting of daily language into a structured database format. Different documents are thus summarized and presented in a uniform manner. Big quantity, high dimensionality and complicated semantics are the difficult issue of document clustering. The aim of this article is primarily to cluster multisense word embedding using three distinct algorithms (K-means, DBSCAN, CURE) using singular value decomposition. In this performance measures are measured using different metrics.
Keywords: SVD, K-means, DBSCAN, CURE.
Scope of the Article: Probabilistic Models and Methods