View in EDS

Text Document Categorization using Modified K-Means Clustering Algorithm

Saved in:
Bibliographic Details
Title: Text Document Categorization using Modified K-Means Clustering Algorithm
Source: International Journal of Recent Technology and Engineering. 8:508-511
Publisher Information: Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP, 2019.
Publication Year: 2019
Description: The volume of the information that is to be managed is increasing at exponential pace. The challenge arises how to manage this large data effectively. There are many parameters on which the performance of such a system can be measured such as time to retrieve the data, similarity of documents placed in same cluster etc. The paper presents an approach for auto-document categorization using a modified k-means. The proposed methodology has been tested on three different data sets. Experimental findings suggest that proposed methodology is accurate and robust for creating accurate clusters of documents. The proposed methodology uses cosine similarity measure and a fuzzy k-means clustering approach to yield the results very fast and accurately.
Document Type: Article
Language: English
ISSN: 2277-3878
DOI: 10.35940/ijrte.b1095.0782s719
Accession Number: edsair.doi...........c702e5f00712e31292d35ef7e90c3dbd
Database: OpenAIRE
Description
Abstract:The volume of the information that is to be managed is increasing at exponential pace. The challenge arises how to manage this large data effectively. There are many parameters on which the performance of such a system can be measured such as time to retrieve the data, similarity of documents placed in same cluster etc. The paper presents an approach for auto-document categorization using a modified k-means. The proposed methodology has been tested on three different data sets. Experimental findings suggest that proposed methodology is accurate and robust for creating accurate clusters of documents. The proposed methodology uses cosine similarity measure and a fuzzy k-means clustering approach to yield the results very fast and accurately.
ISSN:22773878
DOI:10.35940/ijrte.b1095.0782s719