Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner A Comparative Analysis

Decision tree induction and Clustering are two of the most prevalent data mining techniques used separately or together in many business applications. Most commercial data mining software tools provide these two techniques but few of them satisfy business needs.  There are many criteria and factors to choose the most appropriate software for a particular organization. This paper aims to provide a comparative analysis for three popular data mining software tools, which are SAS® Enterprise Miner, SPSS Clementine, and IBM DB2® Intelligent Miner based on four main criteria, which are performance, functionality, usability, and auxiliary Task Support.

[1]  Michael J. A. Berry,et al.  Data mining techniques - for marketing, sales, and customer support , 1997, Wiley computer publishing.

[2]  N. Jovanovic,et al.  Foundations of predictive data mining , 2002, 6th Seminar on Neural Network Applications in Electrical Engineering.

[3]  Ahmed F. Zobaa,et al.  Neural Network Applications in Electrical Engineering , 2007, Neurocomputing.

[4]  Randall Matignon,et al.  Data Mining Using SAS Enterprise Miner , 2007 .

[5]  J. LaFountain.  Inc. , 2013, American Art.

[6]  Karim K. Hirji,et al.  Discovering data mining: from concept to implementation , 1999, SKDD.

[7]  Swagatam Das,et al.  Automatic Clustering Using an Improved Differential Evolution Algorithm , 2007 .

[8]  Daniel S. Tkach.  Information Mining with the IBM Intelligent Miner Family , 1998 .

[9]  Vladimir Estivill-Castro,et al.  Why so many clustering algorithms: a position paper , 2002, SKDD.

[10]  Alfred Ultsch,et al.  Self Organizing Neural Networks perform different from statistical k-means clustering , 2003 .

[11]  A. K. Pujari,et al.  Data Mining Techniques , 2006 .

[12]  Ken W. Collier,et al.  A methodology for evaluating and selecting data mining software , 1999, Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.

[13]  K HirjiKarim.  Discovering data mining , 1999 .