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

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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

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a relationship between items in the same transaction.
Thats 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
customers 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

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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 ...
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