This document discusses data mining and related concepts. It provides an introduction to data mining, explaining that it involves extracting useful information from large amounts of data. It then discusses the key data mining (KDD) processes of data cleaning, integration, transformation, mining, evaluation and presentation. It also covers common data mining techniques like classification, clustering, regression and association rule mining. Finally, it discusses some applications of data mining like market basket analysis, bioinformatics, education and customer relationship management.

1. Presented By:
Suraj R. Bhuyar
M.Sc.-II Computer Science
Post Graduate Department Of Computer Science
Sant Gadge Baba Amravati University,
Amravati.

2. Introduction
There is a huge amount of data available in the
Information Industry. This data is of no use until it is
converted into useful information. It is necessary to
analyze this huge amount of data and extract useful
information from it.
Extraction of information is not only the single process,
data mining also involves other processes such as Data
Cleaning, Data Integration, Data Transformation, Data
Mining, Pattern Evaluation and Data Presentation.
Once all these processes are over, we would be able to use
this information in many applications such as Fraud
detection, Market analysis, Science exploration, etc.

3.  What is Data Mining?
 Why Data Mining?
 What is KDD Process?
 On What Kind of Data?
 Data Mining Techniques
 Data Mining Query Language
 Applications of Data Mining

4. Extraction of interesting
Patterns or Knowledge
from huge amount of data
(Knowledge Discovery
from Data)
One of the Step from KDD
process
What is Data Mining?

5. Why Data Mining?
The Explosive Growth of Data: from
terabytes to petabytes
We are drowning in data, but starving for
knowledge!
Fraud detection and detection of unusual
patterns

6. What is KDD process?
 Data cleaning
to remove noise and inconsistent data
Data integration
where multiple data sources may be combined
Data selection
Related Data
Data transformation
Unified format

7.  Data mining
Extract Patterns
Pattern evaluation
to identify the truly interesting patterns
representing knowledge
Knowledge presentation
Present the mined knowledge to the user

9. On What kind of Data?
Relational Databases
Collection of tables
Data Warehouses
Data from different sources
Transactional Databases
Consists of a file where each record represent
transactions
Advanced Data &Applications
 Multimedia, Spatial data and WWW

10. Data Mining Techniques
Classification
Clustering
Regression
Association Rules

11. Classification
Classification is the process of predicting the class
of a new item.
Therefore to classify the new item and identify to
which class it belongs

12. Clustering
Group Data into Clusters
Similar data is grouped in the same cluster
Dissimilar data is grouped in the same cluster

13. Regression
 “Regression deals with the
prediction of a value, rather
than a class.”
 Regression is a data mining
function that predicts a number
 For example, a regression
model could be used to predict
children's height, given their
age, weight, and other factors.

14. Association Rules
“An association algorithm creates
rules that describe how often events
have occurred together.”
Example: When a customer buys a
Computer, then 90% of the time
they will buy softwares.

15. Data Mining Query Language
A DMQL can provide the ability to supportinteractive
data mining.
Adopts SQL-like syntax
Hence, can be easily integrated with relational query
languages

16. Applications of Data Mining
Market BasketAnalysis
 Market basket analysis is a modeling technique based upon a theorythat
if you buy a certain group of items you are more likely to buy another
group of items.
 This information may help the retailer to know the buyer’s needsand
retailer can enhance the store’s layout
Bio Informatics
 Mining biological data helps to extract useful knowledge frommassive
datasets gathered in biology, and in other related life sciences areas
 Applications of data mining to bioinformatics include
gene finding, protein function inference, disease diagnosis,disease
treatment

17. Education
Data mining can be used by an institution to take accurate
decisions and also to predict the results of the student.
Learning pattern of the students can be captured and used to
develop techniques to teach them.
Customer Relationships Management (CRM)
To maintain a proper relationship with a customer a business
need to collect data and analyze the information.
With data mining technologies the collected data can be
used for analysis.

18. Thank You…