LEARN MORE...

Home
Documents
July 2021
31
8 What is data mining and how is it different from Data Warehouse

8 What is data mining and how is it different from Data Warehouse

Content type

User Generated

Uploaded By

Nouvanixefvatu

Pages

Showing Page:

1/4

8. What is data mining and how is it different from Data

Warehouse. Explain any four data mining techniques

giving examples of the area of use.

Solution

Data warehousing is merely extracting data from different

sources, cleaning the data and storing it in the warehouse.

Where as data mining aims to examine or explore the data

using queries. These queries can be fired on the data

warehouse. data warehousing is a process that must occur

before any data mining can take place. In other words,

data warehousing is the process of compiling and

organizing data into one common database, and data

mining is the process of extracting meaningful data from

that database. The data mining process relies on the data

compiled in the datawarehousing phase in order to detect

meaningful patterns.

data mining techniques :

1) Association

Association is one of the best-known data mining

technique. In association, a pattern is discovered based on

Showing Page:

2/4

a relationship between items in the same transaction.

That’s is the reason why association technique is also

known as relation technique. The association technique is

used in market basket analysis to identify a set of products

that customers frequently purchase together.

Retailers are using association technique to research

customer’s buying habits. Based on historical sale data,

retailers might find out that customers always buy crisps

when they buy beers, and, therefore, they can put beers

and crisps next to each other to save time for customer

and increase sales.

2) Classification

Classification is a classic data mining technique based on

machine learning. Basically, classification is used to

classify each item in a set of data into one of a predefined

set of classes or groups. Classification method makes use

of mathematical techniques such as decision trees, linear

programming, neural network and statistics. In

classification, we develop the software that can learn how

to classify the data items into groups. For example, we can

apply classification in the application that “giv en all

records of employees who left the company, predict who

will probably leave the company in a future period.” In this

case, we divide the records of employees into two groups

that named “leave” and “stay”. And then we can ask our

Showing Page:

3/4

End of Preview - Want to read all 4 pages?

Access Now

Unformatted Attachment Preview

8. What is data mining and how is it different from Data Warehouse. Explain any four data mining techniques giving examples of the area of use. Solution Data warehousing is merely extracting data from different sources, cleaning the data and storing it in the warehouse. Where as data mining aims to examine or explore the data using queries. These queries can be fired on the data warehouse. data warehousing is a process that must occur before any data mining can take place. In other words, data warehousing is the process of compiling and organizing data into one common database, and data mining is the process of extracting meaningful data from that database. The data mining process relies on the data compiled in the datawarehousing phase in orde r to detect meaningful patterns. data mining techniques : 1) Association Association is one of the best-known data mining technique. In association, a pattern is discovered based on a relationship between items in the same transaction. That’s is the reason why association technique is also known as relation technique. The association technique is used in market basket analysis to identify a set of products that customers frequently pur ...
Purchase document to see full attachment