Levelwise Search and Borders of Theories in Knowledge Discovery Levelwise Search and Borders of Theories in Knowledge Discovery

One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for deening subgroups of r, and a selection predicate, nd all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for nding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. For this, we introduce the concept of the border of a theory, a notion that turns out to be surprisingly powerful in analyzing the algorithm. We also consider the veriication problem of a KDD process: given r and a set of sentences S L, determine whether S is exactly the set of interesting statements about r. We show strong connections between the veriication problem and the hypergraph transversal problem. The veriication problem arises in a natural way when using sampling to speed up the pattern discovery step in KDD.