An Efficient Text Classification Scheme Using Clustering. (2016)

• Record Type: Journal Article
• Authors: Thomas, Anisha Mariam; Resmipriya, M.G.
• Is Part Of: Procedia technology. Volume 24(2016)
• Page Start: 1220
• Page End: 1225
• Publication Date: 2016
• Subjects: Data Mining -- Classification -- Semi-supervised Clustering -- Similarity Measures; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.05.095
• Languages: English
• ISSNs: 2212-0173

Abstract

Abstract: Text classification method that uses efficient similarity measures to achieve better performance is being proposed in this paper. Semi-supervised clustering is used as a complementary step to text classification and is used to identify the components in text collection. Clustering makes use of labeled texts to capture silhouettes of text clusters and unlabeled texts to adapt its centroids. The category of each text cluster is labeled by the label of texts in it. Thus here the text clustering is used to generate the classification model for the next text classification step. When a new unlabeled text is incoming, measure its similarity with the centroids of the text clusters and give its label with that of the nearest text cluster. The similarity is calculated using different similarity measures. Results and evaluations are summarized and it is found that the system provides better accuracy when a Similarity Measure for Text Processing (SMTP) used for the distance calculation.