International Journal of Computer
& Organization Trends

Research Article | Open Access | Download PDF

Volume 8 | Issue 2 | Year 2018 | Article Id. IJCOT-V8I2P305 | DOI : https://doi.org/10.14445/22492593/IJCOT-V8I2P305

An Efficient Clustering Process using Optimized C Means Algorithm in Social Media Data

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), vol. 8, no. 2, pp. 34-38, 2018. Crossref, https://doi.org/10.14445/22492593/IJCOT-V8I2P305

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.

Keywords

Clustering, social media, k means algorithm, Manhattan distance, tweeter server, data mining.

References

[1]. Andreas M Kaplan and Michael Haenlein. Users of the world, unite! the challenges and opportunities of social media. Business horizons, 53(1):59–68, 2010.
[2]. Bogdan Batrinca and Philip C Treleaven. Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY, 30(1):89– 116, 2015.
[3]. David Lazer, Alex Sandy Pentland, Lada Adamic, Sinan Aral, Albert Laszlo Barabasi, Devon Brewer, Nicholas Christakis, Noshir Contractor, James Fowler, Myron Gutmann, et al. Life in the network: the coming age of computational social science. Science (New York, NY), 323(5915):721, 2009.
[4]. Claudio Cioffi-Revilla. Computational social science. Wiley Interdisciplinary Reviews: Computational Statistics, 2(3):259–271, 2010.
[5]. Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World Wide Web, pages 591–600. ACM, 2010.
[6]. Michael D Conover, Clayton Davis, Emilio Ferrara, Karissa McKelvey, Filippo Menczer, and Alessandro Flammini. The geospatial characteristics of a social movemen communication network. PloS one, 8(3):e55957, 2013.
[7]. Bruce A. Maxwell, Frederic L. Pryor, Casey Smith, “Cluster analysis in cross-cultural research” World Cultures 13(1): 22-38, 2002.
[8] Kiri Wagstaff and Claire Cardie Department of computer science, Cornell University, USA “Constrained k- means algorithm with background knowledge”.
[9] Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest, Introduction to Algorithms, Prentice Hall, 1990.
[10] Anil K. Jain, M. N. Murty, P. J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, 31(3): 264-323 (1999).
[11]. Bogdan Batrinca and Philip C Treleaven. Social media analytics: a survey of techniques, tools and platforms. AI & SOCIETY, 30(1):89– 116, 2015.
[12]. Emilio Ferrara, Mohsen JafariAsbagh, Onur Varol, Vahed Qazvinian, Filippo Menczer, and Alessandro Flammini. Clustering memes in social media. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 548–555. IEEE, 2013.