Sampling Rank Correlated Subgroups.

Data mining, a key technique in knowledge discovery, is the process of identifying useful patterns from a collection of data. This process is made difficult for complex data combining, for example, numeric and symbolic attributes, or also when the number of observations is large. In this paper, we present a pattern mining approach to identify local correlations in the data, that is to say, sets of numerical attributes that strongly co-vary together in a subset of the data. The sets of numerical attributes and the subset of data are automatically (inductively) identified by the method. Whereas the space of patterns to be potentially explored is exponential, the complexity of the problem can be overcome by using sampling techniques that have several advantages: (1) reducing the computation cost, (2) identifying most important patterns, and (3) making possible to process large databases by distributed computing on multiple machines.