This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.

2. APPLICATION AREAS OF DATA
MINING
PRESENTED BY: PRIYA JAIN
MCA

3. What is DATA MINING?
 Data mining is a process of discovering meaningful new correlations, patterns and trends by
digging into (mining) larger amounts of data stored in warehouses, using artificial intelligence
(AI) and statistical and mathematical techniques.
 By using software to look for patterns in large batches of data, businesses learn more about
their customers and develop more effective marketing strategies as well as increase sales
and decrease cost.
 The main goal of data mining is to extract information from a data set and transform it into
an understandable structure for further use.
 Eg. Grocery stores are well-known users of data mining techniques. Many supermarkets offer
free loyalty cards to customers that give them access to reduced prices not available to non-
members. The cards make it easy for stores to track who is buying what, when they are
buying it and at what price. The stores can then use this data, after analyzing it, for multiple
purposes, such as deciding when to put items on sale or when to sell them at full price.

4. APPLICATIONS AREAS OF DATA MINING
Data Mining Applications in RETAILING.
Data Mining Applications in BANKING.
Data Mining Applications in MEDICAL.
Data Mining Applications in EDUCATION.
Data Mining Applications in INSURANCE.
Data Mining Applications in TRANSPORTATION.
Data Mining Applications in CRIMINAL INVESTIGATION.
Data Mining Applications in TELECOMMUNICATIONS
Data Mining Applications : OtheR ApplicationS.

5. DATA MINING IN RETAILING FIELD
With data mining, a retailer use point-of-sale records of customer purchases to develop products and
promotions to appeal to specific customer segments.
 PERFORMING MARKET BASKET ANALYSIS
Basket Analysis refers to what customers have in their shopping basket when they are shopping. Basket
Analysis way of Data Mining it is based on the assumption that you can predict your future purchased
product depending on your customer behaviour.
Example- a clothing store.
 SALES FORECASTING
Examining time-based patterns helps retailers make stocking decisions. Example: If a customer
purchases an item today, when are they likely to purchase a complementary item?
 DATABASE MARKETING
Retailers develop profiles of customers with certain behaviours. This information can be used to focus
cost–effective promotions.
 MERCHANDISE PLANNING AND ALLOCATION
When retailers add new stores, they improve merchandise planning and allocation by examining
patterns in stores with similar demographic characteristics.

6. DATA MINING IN BANKING FIELD
Data mining is a tool that enable better decision-making throughout the banking and its techniques are very helpful to the
banks for better targeting and acquiring new customers and the analysis of the customers.
 CREDIT CARD AFFILIATION /CARD MARKETING:
 By identifying customer segments, card issuers and acquirers can improve profitability with more effective
acquisition and retention programs, targeted product development, and customized pricing.
 Credit card spending by customer groups can be identified by using data mining.
 CUSTOMER RELATIONSHIP MANAGEMENT:
 Which of my customers are likely to be the most loyal, and which are most likely to leave for a competitor?
 To maintain a proper relationship with a customer the banks collects data and analyse the information. This is where
data mining plays its part. With data mining techniques the collected data is used for analysis.
 example: HDFC BANK
 FRAUD DETECTION in FINANCIAL TRANSACTIONS:
 Which types of transactions are likely to be fraudulent, given the demographics and transactional history of a particular
customer?
 Frauds are enormously costly. By analyzing past transactions that were later determined to be fraudulent, banks can
identify patterns. A perfect fraud detection system should protect information of all the users.
 FINANCIAL PLANNING
Data mining can contribute to solving business problems in banking and finance by finding patterns, causalities,
and correlations in business information and market prices that are not immediately apparent to managers .

7. DATA MINING IN MEDICAL FIELD
 Data mining holds great potential to improve health systems.
 Researchers use data mining approaches like multi-dimensional databases, machine
learning.
 Mining is used to predict the volume of patients in every category.
 Data mining helps healthcare insurers to detect frauds.
 BIOINFORMATICS
Data mining is ideally suited for this since it is data-rich. Mining biological data
helps to extract useful knowledge from massive datasets gathered in biology. Applications
of data mining to bioinformatics include gene finding, disease diagnosis etc.
HUMAN GENETICS
 mining helps tackle the very important goal of accepting. It aims to discover how the
changes in an folks DNA sequence affects the risks of developing common diseases
such as cancer, tumors, the data mining scheme is used to perform this task.
Applications of data mining to bioinformatics include gene finding, disease diagnosis,
disease prognosis, disease treatment optimization, data cleansing,
 For example, microarray technologies are used to predict a patient’s outcome. On the
basis of patients’ genotypic microarray data, their survival time and risk of tumor
metastasis or recurrence can be estimated.

8. Smart Health Prediction Using Data Mining

9. 1.The Health Prediction system is an end user support and online consultation project.
2.This system allows users to get instant guidance on their health issues through an intelligent
health care system online.
3.The system contains data of various symptoms and the disease/illness associated with those
symptoms.
4.It also has an option for users of sharing their symptoms and issues.
5.The system processes those symptoms to check for various illnesses that can be associated
with it.
6.The system is designed to use intelligent data mining techniques to guess the most accurate
illness based on patient’s symptoms.
7.If user’s symptoms do not exactly match any disease in the database, then it is shows the
diseases user could probably have based on his/her symptoms.
8.It also consists of doctor address, contacts along with Feedback and administrator dashboard
for system operations.(diagram previous page).
What is Smart health prediction system?

10. DATA MINING IN INSURANCE FIELD
 The growth of the insurance industry entirely depends on the ability to convert data into the
knowledge, information or intelligence about customers, competitors, and its markets.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 the insurance industry are listed below:
1. Data mining is applied in claims analysis such as identifying which medical procedures are claimed
together.
2. Data mining enables to forecasts which customers will potentially purchase new policies. Another
benefit of data mining for insurance is in it’s ability to help insurers spot patterns which reveal the need
or likelihood of an insured to buy other insurance policies.As an example, a customer who displays a
trend in frequently being involved in auto accidents may be more inclined to purchase a policy which
supplements accidental injuries.
3. data mining for insurance can end the time and headaches associated with attempting to make
distinctions between legitimate and fraudulent insurance claims.By adding automation to fraud
detection, the process becomes significantly less expensive and far more efficient.Ultimately, data
mining detects frauds as it is able to sort through and analyze vast databases n anomalies consistent
with fraud.
 In this way, data mining for insurance can help to uncover fraud cases that may have otherwise
gone undetected.
.

11. DATA MINING IN TRANSPORTATION FIELD
 MONITORING DROWSY DRIVERS
1. Driving when you are sleepy & exhausted? Well, you're as much of a safety hazard as a drunk driver, says the AAA.
And it's not just the AAA who's saying so. Even the NHTSA agrees. In fact, "you're more likely to die from drowsy driving
than from texting while driving, distracted driving or drunk driving combined", according to the CSI Research Center
Here, the data mining techniques are applied to analyse such data, to determine when truck drivers are likely to fall
asleep.
 GLOBAL POSITION SYSTEMS
1. An automated technique such as Global Position System (GPS) has been advocated for navigation applications in vehicles,
and generating detailed maps against the manual lane measurements.
GPS generates position traces with differential corrections. The size of such data is too large and obtaining a refined map
of these traces has been a challenging task.
Data mining approach has been proposed to generate such refined map from GPS data. This approach helps in lane
keeping and convenience applications such as lane-changing advice.
 ROAD ACCIDENTS ANALYSIS
1. While designing the road networks, data related to dangerous and safe stretches are collected. This helps in planning
road improvement schemes.
The data mining of previously collected data of road networks will help in identifying high risk sites inspite of fluctuating
frequency of accidents.

12. DATA MINING IN CRIMINAL INVESTIGATION
 The high volume of crime datasets and also the complexity of relationships between these
kinds of data have made criminology an appropriate field for applying data mining
techniques . Criminology is a process that aims to identify crime characteristics. crime
analysis includes exploring and detecting crimes and their relationships with criminals.
 Identifying crime characteristics is the first step for developing further analysis. The
knowledge that is gained from data mining approaches is a very useful tool which can help
and support police forces.
 Lie Detection : Apprehending a criminal is easy whereas bringing out the truth from him is
difficult. Law enforcement use mining techniques to investigate crimes, monitor
communication of suspected terrorists.

13. DATA MINING AND CRIMINAL INTELLIGENCE
TECHNIQUES
The Coplink project experimented with a variety of the criminal intelligence technique:
1. ENTITY EXTRACTION: Commonly used to automatically identify people, organizations,
vehicles and personal details in unstructured data such as police reports. Even if entity
extraction provides only basic information, it can accelerate the investigation by rapidly
providing precise details from large amounts of unstructured data.
2. CLUSTERING TECHNIQUES: Clustering techniques are used to group similar characteristics
together in classes in order to gain intelligence by maximizing or minimizing similarities; for
example, to identify suspects or criminal groups conducting crimes in similar ways.
3. ASSOCIATION RULES: This data mining technique has been used to discover recurring items
in databases in order to create pattern rules. This technique has been effective in preventing
network intrusions and attacks, such as denial of service attacks.
4. CLASSIFICATION: This technique is useful for analyzing unstructured data to discover
common properties among criminal entities. Classification has been used together with
inferential statistics techniques to predict crime trends.
6. STRING COMPARISON: This technique is used to reveal deceptive information in criminal
records by comparing structured text fields. This requires highly intensive computational
capabilities.

14. DATA MINING IN TELECOMMUNICATION
FIELD
1. CALL DETAIL RECORD ANALYSIS :
Telecommunication companies accumulate detailed call records. By identifying customer
segments with similar use patterns, the companies can develop attractive pricing and feature
promotions. Data Mining can determine characteristic customer clusters on the basis of
collected historic data points from customers – such as for instance the frequency and timely
distribution of customers’ usage of services (calls, text messages, MMS, navigation, mail
exchange,…). For each of these customer patterns the company can then offer tailored
customer-life-cycle messages and offers.
2. CUSTOMER LOYALTY :
Some customers repeatedly switch providers to take advantage of attractive incentives by
competing companies.
The companies can use DM to identify the characteristics of customers who are likely to remain
loyal once they switch, thus enabling the companies to target their spending on customers who
will produce the most profit.

15. DATA MINING IN EDUCATION FIELD
 There is a new emerging field, called Educational Data Mining, concerns with developing
methods that discover knowledge from data originating from educational Environments.
 Educational Data Mining is a term used for processes designed for the analysis of data from
educational settings to better understand students and the settings which they learn in.
 The goals of EDM are identified as predicting students’ future learning behaviour.
 Data mining is used by an institution to take accurate decisions and also to predict the
results of the student . With the results the institution focus on what to teach and how to
teach.
 Learning pattern of the students are captured and used to develop techniques to teach
them.
THE AREAS OF EDM APPLICATION ARE:
 Analysis and visualization of data
 Recommendations for students
 Predicting student performance
 Student modelling
 Detecting undesirable student behaviours
 Grouping students

16. Data Mining Applications: Other
Applications
CUSTOMER SEGMENTATION
All industries take advantage of DM to discover discrete segments in their customer bases by
considering additional variables beyond traditional analysis. Traditional market research may
help us to segment customers but data mining goes in deep and increases market effectiveness.
Data mining aids in aligning the customers into a distinct segment and can tailor the needs
according to the customers. Market is always about retaining the customers. Data mining allows
to find a segment of customers based on vulnerability and the business could offer them with
special offers and enhance satisfaction.
MANUFACTURING
Through choice boards, manufacturers are beginning to customize products for customers;
therefore they must be able to predict which features should be bundled to meet customer
demand.
WARRANTIES
Manufacturers need to predict the number of customers who will submit warranty claims and
the average cost of those claims.
FREQUENT FLIER INCENTIVES
Airlines can identify groups of customers that can be given incentives to fly more.

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