Learning models and related results depend on the quality of the input data. If raw data is not properly cleaned and structured, the results are tending to be incorrect. Therefore, discretization as one of the preprocessing techniques plays an important role in learning processes. The most important challenge in the discretization process is to reduce the number of features’ values. This operat...

The aperiodic Fourier modal method in contrast-field formulation is a numerical discretization and solution technique for solving scattering problems in electromagnetics. Typically, spectral discretization is used in the finite periodic direction and spatial discretization in the orthogonal direction. In the light of the fact that the structures of interest often have a large width-toheight rat...

This paper proposes an experimental evaluation of various discretization schemes in three different evolutionary systems for inductive concept learning. The various discretization methods are used in order to obtain a number of discretization intervals, which represent the basis for the methods adopted by the systems for dealing with numerical values. Basically, for each rule and attribute, one...

Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD...

Investigating nozzle’s orifice flow is challenging both experimentally and theoretically. This paper focuses on simulating flow inside diesel injector nozzle via Ansys fluent v15. Validation is performed with experimental results from Winkhofler et al (2001). Several important parameters such as mass flow rate, velocity profiles and pressure profiles are used for this validation. Results includ...

An implicit unsteady upwind solver including a mesh motion approach was applied to simulate a helicopter including body, main rotor and tail rotor in hover flight. The discretization was based on a second order finite volume approach with fluxes given by the Roeand#39;s scheme. Discretization of Geometric Conservation Laws (GCL) was devised in such a way that the three-dimensional flows on arbi...

Discretization is defined as the process that divides continuous numeric values into intervals of discrete categorical values. In this article, the concept of cost-based discretization as a pre-processing step to the induction of a classifier is introduced in order to obtain an optimal multi-interval splitting for each numeric attribute. A transparent description of the method and the steps inv...

Discretization error occurs during the approximate numerical solution of differential equations. Of the various sources of numerical error, discretization error is generally the largest and usually the most difficult to estimate. The goal of this paper is to review the different approaches for estimating discretization error and to present a general framework for their classification. The first...

Problem statement: Handling numerical data stored in a relational database has been performed differently from handling those numerical data stored in a single table due to the multiple occurrences (one-to-many association) of an individual record in the non-target table and non-determinate relations between tables. Numbers in Multi-Relational Data Mining (MRDM) were often discretized, after co...

Many data mining algorithms require as a pre-processing step the discretization of real-valued data. In this paper we review some discretization methods based on clustering. We describe in detail the algorithms of discretization of a continuos real-valued attribute using the hierarchical graph clustering methods.