Smart Learning in Document Categorization using Dynamic Learning
S. M. Prabin1, N. Selvaganesh2, N. Rajesh Pandian3, T. Selva Kumar4
1S. M. Prabin, Assistant Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.
2 N. Selvaganesh, Assistant Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.
3N. Rajesh Pandian, Assistant Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.
4T. Selva Kumar, Assistant Professor, Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.
Manuscript received on 21 October 2019 | Revised Manuscript received on 25 October 2019 | Manuscript Published on 02 November 2019 | PP: 4076-4081 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B15960982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1596.0982S1119
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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: Clustering is the process of making data groups using similar data items, used for data mining to extract data from available large datasets. A large volume of text documents consisting of personal information is being generated in form of digital libraries and repositories in internet daily.It is conceivable to get to great quality instructive substance and strategies in an increasingly helpful manner. In spite of the fact that a ton of keen instruments have been connected for instructive application, there are just restricted looks into that show the instructive viability of shrewd devices through test contemplations, Clustering organizes large quantity of unordered text documents into small number of meaningful and coherent clusters. A clustering method based on K-Means algorithm is proposed in this paper. K-Means is a unsupervised algorithm based on randomly selected initial centroids used to cluster a highly unstructured and unlabeled document collection. The system will be evaluated using precision as a measure.
Keywords: K-Means, DTM, IDF, Vector Space Model, Document Frequency-Based Selection.
Scope of the Article: Smart Learning Methods and Environments