Performance analysis of data mining tools cumulating with a proposed data mining middleware

Data mining has becoming increasingly popular in helping to reveal important knowledge from the organization's databases and has led to the emergence of a variety of data mining tools to help in decision making. Present study described a test bed to investigate five major data mining tools, namely IBM intelligent miner, SPSS Clementine, SAS enterprise miner, oracle data miner and Microsoft business intelligence development studio. Present studies focus on the performance of these tools. Results provide a review of these tools and propose a data mining middleware adopting the strengths of the tools.

[1]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[2]  David A. Patterson,et al.  Storage performance-metrics and benchmarks , 1993 .

[3]  Vikram Pudi,et al.  Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I , 2010, PAKDD.

[4]  David M. Rocke,et al.  Data Mining Research: Opportunities and Challenges , 2008 .

[5]  Jeffrey W. Seifert,et al.  Data Mining: An Overview , 2004 .

[6]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[7]  Dean Abbott,et al.  An evaluation of high-end data mining tools for fraud detection , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[8]  Gregory Piatetsky-Shapiro,et al.  Advances in Knowledge Discovery and Data Mining , 2004, Lecture Notes in Computer Science.

[9]  Mark Fleming King, John F. , 1995 .

[10]  Le Gruenwald,et al.  A survey of data mining and knowledge discovery software tools , 1999, SKDD.

[11]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[12]  A Young Data mining, text mining and their business applications , 2005 .

[13]  N. F. F. Ebecken,et al.  Data Mining V: Data Mining, Text Mining and Their Business Applications , 2004 .

[14]  Madhu Sudan,et al.  A statistical perspective on data mining , 1997, Future Gener. Comput. Syst..

[15]  Marguerite Summers,et al.  Evaluation of fourteen desktop data mining tools , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).

[16]  David J. Hand,et al.  Statistics and data mining: intersecting disciplines , 1999, SKDD.