Social Media Analysis using Optimized K-Means Clustering IJTSRD
Now a day’s social media place an important role for sharing human social behaviors and participation of multi users in the network. The social media will create opportunity for study human social behavior to analyze 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 behavior is hardly captured by existing methods. In this paper we are proposed optimized fuzzy means clustering 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.
by K. Madhuri | Mr. K. Srinivasa Rao “Social Media Analysis using Optimized K-Means Clustering”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019,
Social Media Analysis using Optimized K-Means Clustering IJTSRD IEEE PAPER
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