This paper presents a Bayesian method for constructing Bayesian belief networks from a database of cases. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. We r...

As drug discovery and translational scientists think about using stratification with biomarkers to improve the chances of getting medicines to patients, several areas of expertise need to be considered. These include analytical validation, clinical validation, regulatory affairs and intellectual property. Getting the right input from the right expert at the right time can make or break an effor...

In this paper we give an overview of four algorithms that we have developed for pattern matching, pattern discovery and data compression in multidimensional datasets. We show that these algorithms can fruitfully be used for processing musical data. In particular, we show that our algorithms can discover instances of perceptually significant musical repetition that cannot be found using previous...

We highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual valuation of k. Manual valuation of k may be interesting for specific domains of applications where the expert has an accurate idea of the number of clusters he wants, howe...

In this paper, we describe the Hugin Tool as an efficient tool for knowledge discovery through construction of Bayesian networks by fusion of data and domain expert knowledge. The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks. The performance of the Hugin Tool is illustrated using real-world Bayesian networks, commonly used exam...

Knowledge extraction from distributed database systems, have been investigated during past decade in order to analyze billions of information records. In this work a competitive deduction approach in a heterogeneous data grid environment is proposed using classic data mining and statistical methods. By applying a game theory concept in a multi-agent model, we tried to design a policy for hierar...

Knowledge Acquisition by the Domain Expert Using the Tool HEMATOOL p. 122 Application of Inductive Logic Programming for Learning ECG Waveforms p. 126 Knowledge Discovery from a Breast Cancer Database p. 130 An Adaptive Two-Tier Menu Approach to Support On-Line Entry of Diagnoses p. 134 Machine Learning Applied to Diagnosis of Sport Injuries p. 138 A Theoretical Framework for Decision Trees in ...

We show how state-of-the-art Semantic Web technology can be used in e-Science, in particular, to automate the classification of proteins in biology. We show that the resulting classification was of comparable quality to that performed by a human expert, and how investigations using the classified data even resulted in the discovery of significant information that had previously been overlooked,...

Health is a complex interaction requiring complex science techniques, say Ana Fernandez and colleagues from the University of Sydney and the University of Newcastle, Australia. They argue that randomised controlled trials (RCTs) can be useful in the discovery phase, but inadequate in the field of implementation. Implementing change in healthcare needs to take into account not only the evidence,...

The rule extraction techniques have been widely developed and used for data mining in many application areas [1], such as medical diagnosis, decision-making, classification and prediction. Rough sets theory, proposed by Zdzislaw Pawlak in the early 1980’s [2], has been used as rule extraction method for machine learning, knowledge discovery, expert systems [3]. This technique has offered useful...