There are many different ways of representing knowledge, and for each of these ways there are many different discovery algorithms. How can we compare different representations? How can we mix, match and merge representations and algorithms on new problems with their own unique requirements? This chapter introduces probabilistic modeling as a philosophy for addressing these questions and presents graphical models for representing probabilistic models. Probabilistic graphical models are a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. 4.1 Introduction Perhaps one common element of the discovery systems described in this and previous books on knowledge discovery is that they are all different. Since the class of discovery problems is a challenging one, we cannot write a single program to address all of knowledge discovery. The KEFIR discovery system applied to health care by Matheus, Piatetsky-Shapiro, and McNeill (199...

The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization fits the way how KEX creates a knowledge base. Nevertheless, the resulting categorization is suitable also for other machine learning algorithms. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is devided into int...

. Unlike on-line discretization performed by a number of machine learning (ML) algorithms for building decision trees or decision rules, we propose o#-line algorithms for discretizing numerical attributes and grouping values of nominal attributes. The number of resulting intervals obtained by discretization depends only on the data; the number of groups corresponds to the number of classes. Since both discretization and grouping is done with respect to the goal classes, the algorithms are suitable only for classification/prediction tasks. As a side e#ect of the o#-line processing, the number of objects in the datasets and number of attributes may be reduced. It should be also mentioned that although the original idea of the discretization procedure is proposed to the Kex system, the algorithms show good performance together with other machine learning algorithms. 1 Introduction The Knowledge Discovery in Databases (KDD) process can involve a significant iteration and may ...

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