Complexity of Rule Sets Induced from Incomplete Data Sets Using Global Probabilistic Approximations

Abstract

We consider incomplete data sets using two interpretations of missing attribute values: lost values and "do not care" conditions. Additionally, in our data mining experiments we use global probabilistic approximations (singleton, subset and concept). The results of validation of such data, using global probabilistic approximations, were published recently. A novelty of this paper is research on the complexity of corresponding rule sets, in terms of the number of rules and number of rule conditions. Our main result is that the simplest rule sets are induced from data sets in which missing attribute values are interpreted as "do not care" conditions where rule sets are induced using subset probabilistic approximations. © Springer International Publishing Switzerland 2014.