Data Mining -- Where to go?

• Question 1:

Is there a future in industrial applications?

 -- When data mining started, part of the hype was that DM would make data analysis simple, with results that could be easily understood. Is that still true for, e.g., ensembles of support vector machines?

 -- In marketing applications it is enough to be better than before (or the competition). Have we gone too far into the search for perfect models in lieu of simple good-enough models?

 -- The quality measures we use in algorithms are often inspired by algorithmic advantages. Are monotone constraints actually important for applications?

• Question 2:

Will Data Mining go down under its own succes?

 -- Data Mining is running the risk of being shattered The number of application areas with their own workshops, conferences and journals is proliferating. Take Bioinformatics as an example. Lots of interesting work is done their. However, it is an autonomous community. Some of our community also publish their results, but people from Bioinformatics don't seem to cross-over into our community. With a few more of such areas, Data Mining will cease to be a homogeneous community.

 -- Similarly, if you want to keep up with all the research going on in Data Mining proper, you also have look at the database conferences and journal, the computational Statistics conferences and journals and so on.

• Question 3:

Are there any interesting problems left?

 -- All major conferences have seen a huge rise in the number of submissions. But many of these papers are epsilon papers. If we tinker a bit more on some aspect, we can get epsilon better results on a certain data set under certain conditions. Researchers have to focus on smaller and smaller subareas to be able to reach any results. This means that conferences give hardly anymore new inspiration and progress is stalled.