Presenting by: Shivani Soni
Course: B-Tech(CSE 7th)
Roll no.: 13/NR/UT/CS005
Presenting to: Mr. Vishal Patyal
O Data Mining
OWhy Data Mining ?
O Where we use data mining ?
O Applications of Data Mining…….
• Data Mining is an interdisciplinary subfield of Computer
• Data Mining is the Computational Process of discovering
Pattern in Large Data Sets involving Method at the intersection
of artificial intelligence , machine learning statistics and
• The Overall goal of the Data Mining process is to
extract information from a data set and transform it
into an understandable structure for further use.
Data Mining is a process that analyse
a large amount of data to find new and
hidden information that improves business .
1. Data Mining Applications in Sales/Marketing
2. Data Mining Applications in Banking / Finance
3. Data Mining Applications in Health Care and Insurance
4. Data Mining Applications in Transportation
5. Data Mining Application in Medicine
6. Data Mining Applications in Education
7. Data Mining Applications in Manufacturing Engineering
8. Research analysis
Data mining enables businesses to understand the hidden
patterns inside historical purchasing transaction data.
Data mining is used for Market Basket Analysis to provide information
on what product combinations were purchased together when they were
bought and in what sequence. This information helps businesses promote
their most profitable products and maximize the profit. In addition, it
encourages customers to purchase related products that they may have
been missed or overlooked.
O Retail companies use data mining to identify customer’s behaviour
OCredit card spending by customer groups can be identified
by using data mining.
OThe hidden correlation’s between different financial
indicators can be discovered by using data mining.
OFrom historical market data, data mining enables to
identify stock trading rules.
O Data mining is used to identify customers loyalty by analysing the
data of customer’s purchasing activities .
OData mining helps determine the distribution
schedules among warehouses and outlets and
analyses loading patterns.
Data mining is applied in insurance industry lately but brought tremendous
competitive advantages to the companies who have implemented it
successfully. The data mining applications in insurance industry are listed
O Data mining is applied in claims analysis such as identifying
which medical procedures are claimed together.
O Data mining enables to forecasts which customers will potentially
purchase new policies.
O Data mining allows insurance companies to detect risky
customers’ behaviour patterns.
O Data mining helps detect fraudulent behaviour.
O Data mining enables to characterize patient activities to see incoming
O Data mining helps identify the patterns of successful medical therapies
for different illnesses.
Example:- Smart Health Prediction in Data Mining
O There is a new emerging field, called Educational Data Mining,
concerns with developing methods that discover knowledge from data
originating from educational Environments. The goals of EDM are
identified as predicting students’ future learning behaviour, studying
the effects of educational support, and advancing scientific knowledge
about learning. Data mining can be used by an institution to take
accurate decisions and also to predict the results of the student. With
the results the institution can focus on what to teach and how to teach.
Learning pattern of the students can be captured and used to develop
techniques to teach them.
O Knowledge is the best asset a manufacturing enterprise would possess.
Data mining tools can be very useful to discover patterns in complex
manufacturing process. Data mining can be used in system-level
designing to extract the relationships between product architecture,
product portfolio, and customer needs data. It can also be used to
predict the product development span time, cost, and dependencies
among other tasks.
History shows that we have witnessed revolutionary changes in research.
Data mining is helpful in data cleaning, data pre-processing and
integration of databases. The researchers can find any similar data from
the database that might bring any change in the research. Identification of
any co-occurring sequences and the correlation between any activities can
be known. Data visualisation and visual data mining provide us with a
clear view of the data.