New Trends in Data Mining

The amount of newly created information increases every year. Large-scale automation projects, the ubiquity of personal computers and the declining prices of storage are all factors that contribute to this trend. The huge amount of information has made it impossible for human analysts to gain a deeper understanding of their data without at least some form of computer-aid. Data mining can be used to automate this process of knowledge discovery from databases. Over the past years, data mining has grown from a relatively unknown technique into a widespread billion dollar business. While data mining was first only adopted in the retail and banking sectors, we can nowadays observe a proliferation of the application domains. In this paper we cover some of these recent application domains and explain how data mining can contribute towards providing new insights and an increased efficiency in these fields. In the second part of this article, we present some new data mining techniques that are expected to make a rapid transition into business environments.

[1]  Saso Dzeroski,et al.  Multi-relational data mining: an introduction , 2003, SKDD.

[2]  Bart Baesens,et al.  Confidence intervals for probabilistic network classifiers , 2005, Comput. Stat. Data Anal..

[3]  Saso Dzeroski,et al.  Inductive Logic Programming and Knowledge Discovery in Databases , 1996, Advances in Knowledge Discovery and Data Mining.

[4]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[5]  Bart Baesens,et al.  Web Usage Mining with Time Constrained Association Rules , 2004, ICEIS.