The week's pick
Reseach Article
Review of Error Rate and Computation Time of Clustering Algorithms on Social Networking Sites
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 113 - Number 8 |
| Year of Publication: 2015 |
| Authors: Jaskaranjit Kaur, Gurpreet Singh |
10.5120/19849-1716
|
Jaskaranjit Kaur, Gurpreet Singh . Review of Error Rate and Computation Time of Clustering Algorithms on Social Networking Sites. International Journal of Computer Applications. 113, 8 ( March 2015), 32-35. DOI=10.5120/19849-1716
Abstract
Data mining is a method of finding useful patters from large volumes of data. It is an extension of traditional data analysis and statistical approaches. Data Clustering is a task of grouping a set of items or objects into subsets (called clusters). It is an algorithm to discover the similarity between objects in the same class (intraclass similarity) and minimizing the similarity between objects of different classes (interclass similarity). This paper discusses the standard KMeans clustering algorithm and Kohonen Self Organizing Map(SOM) clustering algorithm using the Tanagra datamining tool . These algorithms are applied on facebook dataset i. e which type of information is shared by university students on facebook. And that information is then used for product marketing purposes. And according to our analysis SOM gives best result with high accuracy and less computational time.
References
- Shaina Dhingra , RimpleGilhotra,Ravishanker, "Comparative Analysis of Kohonen-SOM and K-Means data mining algorithms based on Academic Activities", 2013 International Journal of Computer Applications(0987-8887)
- RichaDhiman, ShevetaVashisht, "A Cluster analysis and Decision Tree Hybrid Approach in Data Mining to Describe Tax Audit", International Journal of Computers & Technology Volume 4 No. 1, Jan-Feb, 2013
- Saurabh Shah,Manmohan Singh, "Comparison of A Time Efficient Modified K-mean Algorithm with K-Mean and K-Medoid algorithm", 2012 IEEE International Conference on Communication Systems and Network Technologies.
- Y. Ramamohan, K. Vasantharao , C. KalyanaChakravarti , A. S. K. Ratnam, "A Study of Data Mining Tools in Knowledge Discovery Process", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-3, July 2012
- Shi Na , Liu Xumin, Guan yong , "Research on k-means Clustering Algorithm An Improved k-means Clustering Algorithm", 2010 IEEE Third International Symposium on Intelligent Information Technology and Security Informatics.
- SuwimonVongsingthong,NawapornWisitpongphan,"Classification of University Students' Behaviors in Sharing Information on Facebook", 2014 IEEE 11th International Joint Conference on Computer Science and Software Engineering (JCSSE)
- Shalove Agarwal, Shashank Yadav, Kanchan Singh, "K-means versus K-means ++ Clustering Technique", 2012 IEEE Second International Workshop on Education Technology and Computer Science
- W. -L. C. T. -H. Lin, "A Cluster-Based Approach for Automatic Social Network Construction", 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 601 - 606 2010.
- Wei-Lun Chang, Tzu-Hsiang Lin, "A Cluster-Based Approach for Automatic Social Network Construction", 2010 IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust
- S. N. Alsaleh, R. , Yue Xu, "Grouping people in social networks using a weighted multi-constraints clustering method ", 2012 IEEE InternationalConference on Fuzzy Systems (FUZZ-IEEE), pp. 1-8, 2012
- Ying He, Tian-Jin Feng, Jun-Kuo Cao, Xiang-Qian Ding Y, Ing-Hui Zhou, "Research on Some Problems in the Kohonen SOM Algorithm" , IEEE Proceedings of the First International Conference on Maclune Learning and Cybernetics, Beijing, 4-5 November 2002.
- Fernando Bacao, Victor Lobo, Marco Painho, "Self-organizing Maps as Substitutes for K-Means Clustering", Springer-Verlag Berlin Heidelberg 2005.
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