Performance Evaluation of Clustering Algorithms

  ijett-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2013 by IJETT Journal
Volume-4 Issue-7                      
Year of Publication : 2013
Authors : Sharmila , R.C Mishra


Sharmila , R.C Mishra. "Performance Evaluation of Clustering Algorithms". International Journal of Engineering Trends and Technology (IJETT). V4(7):3113-3116 Jul 2013. ISSN:2231-5381. published by seventh sense research group.


Data mining is the process of analysing data from different viewpoints and summarizing it into useful information. Data mining tool allows users to analyse data from different dimensions or angles, categorize it, and précis the relations recognized. Clustering is the important aspect of data mining. It is the process of grouping of data, where the grouping is recognized by finding similarities between data based on their features. Weka is a data mining tool. It provides the facility to classify and cluster the data through machine leaning algorithms. This paper compares various clustering algorithms.


[1] Narendra Sharma, Aman Bajpai , Mr Ratnesh Litoriya, “Comparison the various clustering algorithms of weka tools” 2012.
[2] Dr.N.Rajalingam, K.Ranjini, “Hierarchical C lustering Algorithm - A Comparative Study” 2011.
[3] Bharat Chaudhari, Manan Parikh “A Comparative Study of clustering algorithms using weka tools” 2012.
[4] Manish Verma, Mauly Srivastava, Neha Chack, Atul Kumar Diswar and Nidhi Gupta “A Compara tive Study of Various Clustering Algorithms in Data Mining”, 2012.
[5] Shi Na, L. Xumin, G. Yong, “Research on K - Means clustering algorithm - An Improved K - Means Clustering Algorithm”, “IEEE Third International Symposium on Intelligent Information Technolo gy and Security Informatics”,2010.
[6] D. Napoleon and P. G. Laxmi, “An Efficient K - Means Clustering Algorithm for Reducing Time Complexity using Uniform Distribution Data Points”, “IEEE Trendz in Information science and computing”, Feb.2011.
[7] Eduardo Raul Hruschka , Ricardo J. G. B. Campello , Alex A. Freitas, and Andre C. Ponce Leon F. de Carvalho ,” A Survey of Evolutionary Algorithms for Clustering”, IEEE Transction , 2009 .
[8] Jiawei Han, Micheline Kamber,”Data Mining:Concepts and Techniques” , 2006 .
[9] Zhang, T., Ramakrishnan, R., Linvy , BIRCH : “An Efficient Data Clustering Method for Very Large Databases”. ACM SIGMOD International Conference on Management of Data , 1996.
[10] Guha, S., Rastogi, R., Shim, K , “An Efficient Clustering Algorithms fo r Large Database ” ACM SIGMOD International Conference on Management of Data , 1998 .
[11] Yedla M, Pathakota SR and Srinivasa Enhancing “K - means clustering algorithm with improved initial center” Intl Journal of Computer Science , 2010 .
[12] R.Xu and D. Wun sch, “Survey of Clustering Algorithms”, “IEEE Transactions on Neural networks”, May 2005.
[13] Joshua Zhexue Huang, Michael K. Ng, Hongqiang Rong, and Zichen Li, “Automated Variable Weighting in k - Means Type Clustering”, IEEE Transactions on Pattern Analy sis and Machine Intelligence, 2005.
[14] Ran Vijay Singh, M.P.S Bhatia, “Data Clustering with Modified K - means Algorithm” IEEE - International Conference on Recent Trends in Information Technology, 2011.

Data mining algorithms, Weka tool, K - means algorithm, Clustering methods.