Most integrate with standard commercial database offerings from companies like Sybase, Informix, IBM, and Microsoft. A large number of commercial tools also provide interfaces to popular desktop programs such as Microsoft's Word, Excel, PowerPoint, and any ODBC-compliant database. A Gathering of Quants Six years ago at the first KDD conference, there was virtually no industry representation. Sessions focused on which algorithm or particular method was faster or more effective at finding patterns. Data mining was considered an esoteric activity, confined largely to academic circles and research departments within large corporations.
Data mining is now big business.
One reason that data mining has taken some time to find its way into mainstream IT departments is that it is so quantitatively focused. The tools that have been available until recently required a strong understanding of statistics and other linear and nonlinear methods of data modeling and pattern recognition. And talent in this field is scarce and expensive. Data mining is an interdisciplinary field, with strong quantitative roots. People have come into this field with varying backgrounds in neural networks, statistical analysis, evolutionary computation, fuzzy logic, decision trees, memory-based reasoning, and genetic algorithms.