Data reduction ppt

What do you mean by data reduction?
Data reduction is a capacity optimization technique in which data is reduced to its simplest possible form to free up capacity on a storage device.
There are many ways to reduce data, but the idea is very simple—squeeze as much data into physical storage as possible to maximize capacity..
What is data reduction technique?
Data reduction is a method of reducing the size of original data so that it may be represented in a much smaller space.
While reducing data, data reduction techniques preserve data integrity.
The time spent on data reduction should not be overlooked in favour of the time saved by data mining on the smaller data set..
The goal of data reduction is to present and define the data in a concise manner.
In a nutshell: Data Reduction is a way to attain a compressed version or representation of the data with less volume.

Advantages

Improved efficiency: Data reduction can help to improve the efficiency of machine learning algorithms by reducing the size of the dataset.
This can make it faster and more practical to work with la.

Introduction

Data reduction is a technique used in data mining to reduce the size of a dataset while still preserving the most important information.
This can be beneficial in situations where the dataset is too large to be processed efficiently, or where the dataset contains a large amount of irrelevant or redundant information.

What are the advantages and disadvantages of data reduction?

Reduced storage costs:

Data reduction can help to reduce the storage costs associated with large datasets by reducing the size of the data

Improved interpretability:Data reduction can help to improve the interpretability of the results by removing irrelevant or redundant information from the dataset.
Disadvantages:.

What are the basic principles of data reduction?

Data reduction can be obtained by assuming a statistical model for the data.
Classical principles of data reduction include:

sufficiency
likelihood
conditionality and equivariance

^ "Travel Time Data Collection Handbook" (PDF).

What is number reduction technique?

4.
Numerosity Reduction:

In this reduction technique
the actual data is replaced with mathematical models or smaller representations of the data instead of actual data
it is important to only store the model parameter

Or non-parametric methods such as:clustering, histogram, and sampling. 5.
Discretization & Concept Hierarchy Operation:.

How do data scientists approach data reduction?

To approach data reduction, there are several techniques that data scientists deploy: Organization and Sanitation: The simplest form of data reduction is to clean and sanitize it

How does dimension reduction work?

Dimension reduction reduces this issue through a couple of potential techniques: Feature Selection: This process removes redundant or irrelevant data attributes without affecting the data set

This approach can include proxy models to evaluate the ML model and its data needs or training systems to recognize features with little or no mistakes

What is data reduction?

Data Reduction Data Reduction techniques can be applied to obtain a reduced representation of the data set that is much smaller in volume, yet closely maintains the integrity of the original data

That, is, Mining on the reduced data set should be more efficient yet produce the same analytical results

3 Data Reduction Strategies 1