Classification Connection of Twitter Data using K-Means Clustering
Rashmi H Patil1, Siddu P Algur2
1Rashmi H Patil, Department of Computer Science, Rani Chennamma University, Belagavi, India.
2Siddu P Algur, Department of Computer Science, Rani Chennamma University, Belagavi, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 14-22 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10040486S419/19©BEIESP | DOI: 10.35940/ijitee.F1004.0486S419
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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: The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.
Keywords: Classification of Twitter data, K-Means Clustering, Euclidian Distance, TF/IDF, Social Media.
Scope of the Article: Classification