Research and Optimization of Data Classification using K-means Clustering and Affinity Propagation Technique
P N Varalakshmi K1, Jagadish S Kallimani2
1P N Varalakshmi K, Research Scholar, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
2Jagadish S Kallimani, Associate Professor, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 12 April 2019 | Manuscript Published on 26 July 2019 | PP: 55-60 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F10110486S419/19©BEIESP | DOI: 10.35940/ijitee.F1011.0486S419
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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: Amongst various social networking platforms available in this digital millennium, Twitter facilitates a huge platform to accomplish analysis on data with respect to trends, events, personalities etc. Twitter facilitates the analysts in fetching essential information of the population based on their likes and preferences. Clustering technique is one of the prominent techniques available to fetch the essential data from the massive data being populated. Several clustering methods are available to achieve the objective of grouping the data. This paper throws light on the performance and efficiency of several algorithms used in determining the trending pulses effectively. The clusters of data obtained after clustering are further subjected to classification based on the topics for real time analysis. This paper discusses the flaws obtained in the classification of the data. The data is again subjected to an optimized classification technique and analyzed against the clusters of data.
Keywords: Data Classification, K-means Clustering, Affinity Propagation Technique, Centroid, Euclidian Distance, Classification of Medical Data, TF/IDF.
Scope of the Article: Classification