Decision Tree Induction & Clustering Techniques in Sas Enterprise Miner, Spss Clementine, and Ibm Intelligent Miner – a Comparative Analysis
International Journal of Management & Information Systems – Third Quarter 2010
Volume 14, Number 3
Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner – A Comparative Analysis
Abdullah M. Al Ghoson, Virginia Commonwealth University, USA
ABSTRACT 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. Keywords: Data mining, classification, decision tree, clustering, software evaluation, SAS Enterprise Miner, SPSS Clementine, IBM Intelligent miner, Comparative Analysis, evaluation criteria.
1.
INTRODUCTION
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usinesses face challenges such as growth, regulations, globalization, mergers and acquisitions, competition, and economic changes, which require fast and good decisions rather than guess work. Taking good decisions requires accurate and clear analysis such as prediction, estimation, classification, or segmentation using data mining techniques. Decision tree induction and Clustering are two of the most important data mining techniques that find interesting patterns. There are many commercial data mining software in the market, and most of them provide decision trees induction and clustering data mining techniques. There is no doubt that commercial data mining software are expensive and costly, and choosing one of them is crucial and difficult decision. Therefore, this paper objective is to help
Volume 14, Number 3
Decision Tree Induction & Clustering Techniques In SAS Enterprise Miner, SPSS Clementine, And IBM Intelligent Miner – A Comparative Analysis
Abdullah M. Al Ghoson, Virginia Commonwealth University, USA
ABSTRACT 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. Keywords: Data mining, classification, decision tree, clustering, software evaluation, SAS Enterprise Miner, SPSS Clementine, IBM Intelligent miner, Comparative Analysis, evaluation criteria.
1.
INTRODUCTION
B
usinesses face challenges such as growth, regulations, globalization, mergers and acquisitions, competition, and economic changes, which require fast and good decisions rather than guess work. Taking good decisions requires accurate and clear analysis such as prediction, estimation, classification, or segmentation using data mining techniques. Decision tree induction and Clustering are two of the most important data mining techniques that find interesting patterns. There are many commercial data mining software in the market, and most of them provide decision trees induction and clustering data mining techniques. There is no doubt that commercial data mining software are expensive and costly, and choosing one of them is crucial and difficult decision. Therefore, this paper objective is to help
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