An Efficient Clustering Process using Optimized C Means Algorithm in Social Media Data
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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2017 by IJCOT Journal | ||
| Volume - 8 Issue - 2 |
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| Year of Publication : 2018 | ||
| Authors : Aratakatla Hari Kusuma, P. Mohana Roopa |
Citation
Aratakatla Hari Kusuma, P. Mohana Roopa"An Efficient Clustering Process using Optimized C Means Algorithm in Social Media Data", International Journal of Computer & organization Trends (IJCOT), V8(2):34-38 March - April 2018, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract
Now a day’s social media place an important role for sharing human social behaviour’s and participation of multi users in the network. The social media will create opportunity for study human social behaviour to analyse large amount of data streams. In this social media one of the interesting problems is users will introduce some issues and discuss those issues in the social media. So that those discuss will contain positive or negative attitudes of each user in the social network. By taking those problems we can consider formal interpretation social media logs and also take the sharing of information that can spread person to person in the social media. Once the social media of user information is parsed in the network and identified relationship of network can be applied group of different types of data mining techniques. However, the appropriate granularity of user communities and their behaviour is hardly captured by existing methods. In this paper we are proposed optimized fuzzy means cluster distance algorithm for grouping related information. By implementing this algorithm we can get best group result and also reduce time complexity for generating cluster groups. The main goal of our proposed framework is twofold for overcome existing problems. By implementing our approach will be very scalable and optimized for real time clustering of social media.
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Keywords
Clustering, social media, k means algorithm, Manhattan distance, tweeter server, data mining.