We may have fairly good statistical methods designed for purely continuous or purely qualitative variables, but not when we have a mixture of continuous and discrete valued random variables. Data mining methods can handle such situations in a better way. Data mining methods seem to be appropriate when we have large data bases, simple models are not adequate and the relation between class and feature variables is complex or poorly understood. At present data mining practitioners and statisticians seem to have different approaches to solving problems of a similar nature. It appears that statisticians and data miners can profit by studying each other's methods and using a judiciously chosen combination of them.
Data mining is an extraordinarily broad area and is growing in several directions.
The proposed speakers have varied backgrounds such as Engineering, Computer Science and Statistics. The session consisting of 4 invited talks will provide a multidisciplinary forum to explore and advance different aspects of data mining, including business, statistics, computer science.