- Why is discretization needed?
- What does FEM mean?
- What is supervised discretization?
- Why is binning needed?
- What are the techniques of data discretization?
- Where is FEA used?
- What is meant by discretization?
- What is discretization in data mining?
- What is meant by finite element?
- What is discretization in machine learning?
- What is difference between FEM and FEA?

## Why is discretization needed?

Discretization is required for obtaining an appropriate solution of a mathematical problem.

It is used to transform the initially continuous problem which has an infinite number of degrees of freedom (e.g.

eigenfunctions, Green’s functions) into a discrete problem where the degree of freedom is inevitably limited..

## What does FEM mean?

Meaning of fem. fem. adjective. language specialized. written abbreviation for feminine or female.

## What is supervised discretization?

Supervised discretization is when you take the class into account when making discretization boundaries, which is often a good idea. It’s important that the discretization is determined solely by the training set and not the test set.

## Why is binning needed?

Binning is a way to group a number of more or less continuous values into a smaller number of “bins”. For example, if you have data about a group of people, you might want to arrange their ages into a smaller number of age intervals. … The data table contains information about a number of persons.

## What are the techniques of data discretization?

– A typical discretization process generally consists of four steps : (1) sorting the continuous values of the feature to be discretized, (2) evaluating a cut point for splitting or adjacent intervals for merging, (3) splitting or merging intervals of continuous values according to some defined criterion.

## Where is FEA used?

These days FEA is being used in virtually every engineering discipline: aerospace, automotive, biomedical, chemicals, electronics, energy, geotechnical, manufacturing, and plastics industries all routinely apply Finite Element Analysis.

## What is meant by discretization?

In applied mathematics, discretization is the process of transferring continuous functions, models, variables, and equations into discrete counterparts. This process is usually carried out as a first step toward making them suitable for numerical evaluation and implementation on digital computers.

## What is discretization in data mining?

Discretization is the process of putting values into buckets so that there are a limited number of possible states. … If your data mining solution uses relational data, you can control the number of buckets to use for grouping data by setting the value of the DiscretizationBucketCount property.

## What is meant by finite element?

The finite element method (FEM) is the most widely used method for solving problems of engineering and mathematical models. … The FEM is a particular numerical method for solving partial differential equations in two or three space variables (i.e., some boundary value problems).

## What is discretization in machine learning?

In statistics and machine learning, discretization refers to the process of converting or partitioning continuous attributes, features or variables to discretized or nominal attributes/features/variables/intervals. This can be useful when creating probability mass functions – formally, in density estimation.

## What is difference between FEM and FEA?

Finite Element Method (FEM) refers mostly to complex mathematical procedures used in your favorite solver. Think about it like a theory manual, lots of equations and mathematics. Finite Element Analysis (FEA) is usually used in the context of applying FEM to solve real engineering problems.