The authors' perspective of database mining as the confluence of machine learning techniques and the performance emphasis of database technology is presented. Three classes of database mining problems involving classification, associations, and sequences are described. It is argued that these problems can be uniformly viewed as requiring discovery of rules embedded in massive amounts of data. A model and some basic operations for the process of rule discovery are described. It is shown how the database mining problems considered map to this model, and how they can be solved by using the basic operations proposed. An example is given of an algorithm for classification obtained by combining the basic rule discovery operations. This algorithm is efficient in discovering classification rules and has accuracy comparable to ID3, one of the best current classifiers.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
The aim of this paper is to evaluate the performance of genetic algorithms for the flowshop scheduling problem with an objective of minimizing the makespan. First we examine var...
An original method for integrating artificial neural networks (ANN) with hidden Markov models (HMM) is proposed. ANNs are suitable for performing phonetic classification, wherea...
In the context of discrete event simulation, the marking of a stochastic Petri net (SPN) corresponds to the state of the underlying stochastic process of the simulation and the ...
This paper traces the origins and applications of HIPERLAN, a system for high speed short range radio networks for computers. HIPERLAN, HIgh Performance European Radio LAN, is t...
A method is described for obtaining tight closed-form bounds on the probability of error for M-ary phase-shift keying (M-PSK) and M-ary differential phase-shift keying (M-DPSK) ...
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