There are many ways to mine your data. Your scope and requirements of the project will dictate the type of mining tool needed. A mining project around discovering the unknown will be different from a predictive orientated mining project. Your scope may be the simple task of identifying which data is the valuable data. A huge cost savings can be attained by knowing which data is driving the decision making process!
R. Golan (1999) "A Methodology for creating a Data Visualization application for Performance Monitoring of Chase's CD Financial Data", in Proceeding of DSI's International Conference. Athens, Greece. July 6th.
R. Golan (1996) "The Rough Approach to Knowledge Marts with Data Warehouses", in Proceedings of International Conference on Rough Sets, Fuzzy Sets, and Machine Discovery. Tokyo, Japan. Nov 7th.
R. Golan (1995) "Thesis: Stock Market Analysis Utilizing Rough Set Theory",
University of Regina, Saskatchewan, Canada. Defense Date was February 11.
R. Golan and W. Ziarko (1995) "A Methodology for Stock Market Analysis utilizing Rough Set Theory" in Proceedings of Computational Intelligence for Financial Engineers. CIFEr-95. New York, USA. April 9th.
R. Golan (1994) "Techniques for Rule Generation Verification" in Proceedings of International Workshop on Rough Sets and Soft Computing. San Jose, USA.
R. Golan and D. Edwards (1993) "Temporal Rules Discovery using Datalogic/R+ with Stock Market Data", in International Conference on Rough Sets and Knowledge Discovery. Banff, Canada. October 12th.
W. Ziarko and R. Golan and D. Edwards (1993) "An Application of Datalogic/R Knowledge Discovery Tool to Identify Strong Predictive Rules in Stock Market Data", in AAAI-93, Workshop on Knowledge Discovery in Databases. Washington, DC. USA.