This document talks about the data mining applications and what is data mining.

1. Data Mining and their
applications
By-
Kushagra Sharan
Shashwat Shankar
Amrendra Roy

2. Understanding
the Data Mining

3. What Can Data Mining Do?
Specific uses of data mining include:-
● Market segmentation
● Customer churn - Predict which customers are likely to leave your company and go to a
competitor.
● Fraud detection - Identify which transactions are most likely to be fraudulent.
● Direct marketing - Identify which prospects should be included in a mailing list to obtain
the highest response rate.
● Interactive marketing - Predict what each individual accessing a Web site is most likely
interested in seeing.
● Market basket analysis - Understand what products or services are commonly purchased
together; e.g., beer and diapers.
● Trend analysis - Reveal the difference between a typical customer this month and last.

4. Data Mining Technologies
Some of the tools used for data mining are:-
● Artificial neural networks - Nonlinear predictive models that learn through training
and resemble biological neural networks in structure.
● Decision trees - Tree-shaped structures that represent sets of decisions. These
decisions generate rules for the classification of a dataset.
● Rule induction - The extraction of useful if-then rules from data based on statistical
significance.
● Genetic algorithms - Optimization techniques based on the concepts of genetic
combination, mutation, and natural selection.
● Nearest neighbor - A classification technique that classifies each record based on
the records most similar to it in an historical database.

5. Virgin mobiles- A case study
The marketing wing of Virgin mobiles service, provides who wants to increase revenues
of long distance services. For high ROI on his sales and marketing efforts customer
profiling is important. He has a vast data pool of customer information like age, gender,
income, credit history, etc. But it's impossible to determine characteristics of people who
prefer long distance calls with manual analysis. Using data mining techniques, he may
uncover patterns between high long distance call users and their characteristics.
For example, he might learn that his best customers are married females between the
age of 45 and 54 who make more than $80,000 per year. Marketing efforts can be
targeted to such demographic.

6. Implementation Process
Data
understanding
Data
transformation
Business
understanding
Modelling
Evaluation
Data preparation

7. Technique used
K means clustering
Cluster analysis or clustering is the task of grouping a set of objects in such a
way that objects in the same group (called a cluster) are more similar (in some
sense) to each other than to those in other groups (clusters).

9. J.P.Morgan- A Case Study
● JP Morgan implemented a unique program called Contract Intelligence
(COIN).
● It is an AI system that is powered to process and analyze documents faster.
● COIN was able to go through and analyze 12,000 annual commercial credit
agreements in a few seconds.
● Earlier when it was done manually, JP Morgan employees would take
360,000 hours to analyze the documents mentioned above.

10. What is Contract Intelligence?
Contract intelligence exists within a contract management platform, a
tool that helps businesses to organize and manage their contracts.
Contract management spans the entire lifecycle of every agreement —
from discovery, uploading and data extraction to authorizing, negotiation,
approval, execution, monitoring and reporting, amendment, renewal and,
eventually, archiving.

12. Continued...
Artificial intelligence helps to uncover the value in this data by enabling
data-driven insights and turning previously static contracts into live
documents that can interact with the surrounding system, people and
other contracts.

14. Summary
● Data Mining is all about explaining the past and predicting the future for analysis.
● Data mining process includes business understanding, Data Understanding, Data
Preparation, Modelling, Evolution, Deployment.
● R-language and Oracle Data mining are prominent data mining tools.
● Data mining technique helps companies to get knowledge-based information.
● The main drawback of data mining is that many analytics software is difficult to
operate and requires advance training to work on.

15. Thank you.