An evaluation of high-end data mining tools for fraud detection

Data mining tools are used widely to solve real-world problems in engineering, science and business. As the number of data mining software vendors increases, however, it has become more challenging to assess which of their rapidly-updated tools are most effective for a given application. Such judgement is particularly useful for the high-end products, due to the investment (money and time) required to become proficient in their use. Reviews by objective testers are very useful in the selection process, but most published to date have provided somewhat limited critiques and haven´t uncovered the critical benefits and shortcomings which can probably only be discovered after using the tool for an extended period of time on real data. In this paper, five of the most highly acclaimed data mining tools [Clementine 4.0 from Integral Solutions Ltd.; Darwin 3.0.1 from Thinking Machines Corp.; Enterprise Miner (EM) from SAS Institute; Intelligent Miner for Data (IM 2.0) from IBM; and Pattern Recognition Workbench (PRW 2.5) from Unica Technologies Inc.] are so compared on a fraud detection application, with descriptions of their distinctive strengths and weaknesses, and lessons learned by the authors during the process of evaluating the products