From unsupervised learning to data mining: linking cognition and data analysis

Recently, Knowledge Discovery on Databases (KDD) has emerged as a promising research area encompassing methods from several disciplines. Particularly, the data mining step of KDD shares most of its goals with unsupervised learning. But data mining methods are biased towards statistical techniques arguing that Machine Learning (ML) methods are not suitable to deal with real-world databases. We claim that (a) the problems of ML systems in dealing with databases may come from their traditional symbolic nature, (b) some ML challenges may be interpreted from a data mining standpoint, and, as a consequence, (c) ML methods which make use of statistical concepts may be good candidates to solve data mining problems, still incorporating characteristic symbolic biases. Conversely, we also suggest the use of ML biases to constraint existing data mining techniques, thus bridging purely statistical techniques and more heuristic {and cognitive-inspired{ ML ones. The unsupervised learning system ISAAC is described to show an application of the proposed ideas.