This ppt briefs the data mining techniques with suitable examples

2. WHAT IS DATA MINING
ELEMENTS
TECHNIQUES
APPLICATIONS

3. DATA
MINING
ELEMENTS TECHNIQUES APPLICATIONS
Data mining (knowledge discovery from data)
Extraction of interesting (non-trivial, implicit, previously unknown
and potentially useful) patterns or knowledge from huge amount of
data
Alternative names
Knowledge discovery (mining) in databases (KDD), knowledge
extraction, data/pattern analysis, data archeology, data dredging,
information harvesting, business intelligence, etc.

4. DATA
MINING
KDD
ELEMENTS TECHNIQUES APPLICATIONS
Data Mining – Core
of Knowledge
Discovery Process
(KDD)

5. DATA
MINING
ELEMENTS
TECHNIQUES/
ALGORITHMS
APPLICATIONS
Data Relationships
 Sequential
Patterns
 Clusters
Data Mining Techniques
 Decision Trees
 Neural Networks
 Regression
 Association Rules
 Nearest Neighbor Method
 Genetic Algorithm
 Artificial Intelligence

6. DATA
MINING
ELEMENTS
TECHNIQUES/
ALGORITHMS
APPLICATIONS
DATA RELATIONSHIPS
Sequential Patterns
 Finding statistically relevant patterns between data
examples where discrete values delivered in sequence.
 Problems addressed
Building efficient databases, indexes for sequence
information, extracting frequently occurring patterns,
comparing sequences, recovering missing sequence
members.
 Application:
{Retail Environment}
Anticipating customer behavior for prediction of future
customer purchasing habits.
Increase profit, Decrease cost : Proper management of
shelf space allocation & products display.

7. DATA
MINING
ELEMENTS
TECHNIQUES/
ALGORITHMS
APPLICATIONS
DATA RELATIONSHIPS
Sequential Patterns {Eg.:}

8. DATA
MINING
ELEMENTS
TECHNIQUES/
ALGORITHMS
APPLICATIONS
DATA RELATIONSHIPS
Clusters
Placing data elements into
related groups without advance
knowledge of the group definitions.
Popular clustering
techniques: K-means,
Expectation Maximization (EM)
Problems addressed
 Find natural groupings
 Preprocess data to
identify homogeneous groups
on which to build supervised
models.
 Anomaly detection

9. DATA
MINING
ELEMENTS
TECHNIQUES/
ALGORITHMS
APPLICATIONS
DATA RELATIONSHIPS
Clusters
Application:
 Plant and animal ecology
Make spatial and temporal comparisons of
communities of organisms in heterogeneous environments
 Medical imaging
differentiate between different types
of tissue and blood in a three-dimensional image
 Business and marketing
Partition the general population of consumers for use
in market segmentation, product positioning, new product
development and Selecting test markets.

10. DATA
MINING
Decision Trees
ELEMENTS
 In decision tree technique, the root of the decision tree is a simple
question or condition that has multiple answers.
 Each answer then leads to a set of questions or conditions that help
us determine the data so that we can make the final decision based
on it.
 For example, we use the following decision tree to determine
whether or not to play tennis
TECHNIQUES/
ALGORITHMS
APPLICATIONS
 Starting at root node, if the outlook
is overcast then we should
definitely play tennis.
 If it is rainy, we should only play
tennis if the wind is week.
 If it is sunny then we should play
tennis in case the humidity is
normal

11. DATA
MINING
ELEMENTS
Neural Networks
TECHNIQUES/
ALGORITHMS
 Set of connected input/output units and each connection has a
weight present with it. During the learning phase, network learns by
adjusting weights so as to be able to predict the correct class labels
of the input tuples.
 Well suited for continuous valued inputs andoutputs
 Used to extract patterns and detect trends that are too complex to be
noticed by.
 Neural networks are best at identifying patterns or trends in data and
well suited for prediction of forecasting needs.
APPLICATIONS
Example :
Handwritten character reorganization, for training a computer to
pronounce English text and many real world business problems and
have already been successfully applied in many industries.

12. DATA
MINING
Regression
ELEMENTS
 Regression technique can be adapted for predication
 Regression analysis can be used to model the relationship between
one or more independent variables and dependent variables. In data
mining independent variables are attributes already known and
response variables are what we want to predict.
 However, it cannot be used for areas involving complex variables like
in sales volumes, stock prices and product failure rates.
Types of regression methods
 Linear Regression
 Multivariate Linear Regression
 Nonlinear Regression
 Multivariate Nonlinear Regress
TECHNIQUES/
ALGORITHMS
APPLICATIONS

13. DATA
MINING
ELEMENTS
Association Rules
TECHNIQUES/
ALGORITHMS
APPLICATIONS
 In association, a pattern is discovered based on a relationship
between items in the same transaction.
 E.g. Rule Form : “Body  Head [support, confidence]”
Application
Retailers are using association technique to research
customer’s 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.
Types
 Multilevel association rule
 Multidimensional association
rule
 Quantitative association rule

14. DATA
MINING
ELEMENTS TECHNIQUES APPLICATIONS
 Study of frequent flyer data from an Indian Airline
 Data selected, prepared : 3 most common sectors flown & points
redeemed for.
(Note :Incomplete/Inaccurate Data supplied by airlines)
 Data Mining results:
 Patterns about customers flying between metropolitan cities
 Customers that flew between Mumbai-Delhi also flew to other cities
like Mumbai-Chennai, Mumbai-Kolkata & Mumbai Bangalore.
 Customers flying Bangalore-Hyderabad also flew Delhi-Bangalore
 Those who flew Bagdogra - Guwahati did not fly back; instead flew to
Delhi

15. DATA
MINING
ELEMENTS TECHNIQUES APPLICATIONS
 Banking information systems contains huge volumes of data both
operational and historical.
 Data mining can assist critical decision making processes in a bank.
 Areas of application:
 Marketing
 Risk management and
default detection
 Fraud detection
 Customer relationship
management
 Money laundering detection

16. DATA
MINING
 Wikipedia
ELEMENTS TECHNIQUES APPLICATIONS
 http://en.wikipedia.org/wiki/Sequential_Pattern_Mini
ng
 http://searchbusinessintelligence.techtarget.in/
 Indian Journal of Computer Science & Engg
 Introduction to Data Mining with Case Studies by
G.K. Gupta