The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Methods and Problems in Data Mining
ICDT '97 Proceedings of the 6th International Conference on Database Theory
A Methodology for Evaluating and Selecting Data Mining Software
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Use of data mining techniques to model crime scene investigator performance
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Older drivers and accidents: A meta analysis and data mining application on traffic accident data
Expert Systems with Applications: An International Journal
Targeting customers via discovery knowledge for the insurance industry
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
The ever-increasing number of fielded Data Mining applications is evidence that the technology works and produces added value in a variety of business areas. Most of the research-lab generated algorithms have found their way under various guises in a number of commercial software packages. When considering the use of Data Mining, the average business user is now faced with a plethora of DM software to choose from. In order to be informed, such a choice requires a standard basis from which to compare and contrast alternatives along relevant, business-focused dimensions, as well as the location of candidate tools within the space outlined by these dimensions. This paper aims at meeting this business requirement. It presents a standard schema for the characterisation of Data Mining software tools and the results of a recent survey of 41 popular Data Mining tools described within this schema.