Abstract
Classification and clustering are powerful and popular data mining techniques. Organizations use them to capture information, retain customers, and improve business performance. This paper presents a method for selecting data mining software for an academic environment based on its classification and clustering tools. This research applies the data mining software evaluation framework to evaluate three major commercial data mining software: SAS Enterprise Miner, Clementine from SPSS, and IBM DB2 Intelligent Miner. We added to the framework a criterion that became important in the Internet age. After ranking software on relevant criteria in the framework then purchase the best one that is affordable for academic support.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
