Performance Evaluation of Clustering Algorithms

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

[1]  Bharat Chaudhari,et al.  A Comparative Study of clustering algorithms Using weka tools , 2012 .

[2]  Manish Verma,et al.  A Comparative Study of Various Clustering Algorithms in Data Mining , 2012 .

[3]  V. K. Bhuvaneswari,et al.  A Comparative Study of Various Clustering Algorithms in Data Mining , 2014 .

[4]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[5]  M. P. S Bhatia,et al.  Data clustering with modified K-means algorithm , 2011, 2011 International Conference on Recent Trends in Information Technology (ICRTIT).

[6]  Michael K. Ng,et al.  Automated variable weighting in k-means type clustering , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Guan Yong,et al.  Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm , 2010, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics.

[8]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[9]  D. Napoleon,et al.  An efficient K-Means clustering algorithm for reducing time complexity using uniform distribution data points , 2010, Trendz in Information Sciences & Computing(TISC2010).

[10]  Narendra Sharma,et al.  Comparison the various clustering algorithms of weka tools , 2012 .

[11]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[12]  Chen Zhang,et al.  K-means Clustering Algorithm with Improved Initial Center , 2009, 2009 Second International Workshop on Knowledge Discovery and Data Mining.

[13]  Madhu Yedla,et al.  Enhancing K-means Clustering Algorithm with Improved Initial Center , 2010 .

[14]  K Ranjini,et al.  Hierarchical Clustering Algorithm - A Comparative Study , 2011 .

[15]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[16]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.