Data mining concepts

Mar 16, 2012Download as PPTX, PDF2 likes474 views

Data mining involves discovering patterns and trends in large data sets. It uses techniques from statistics, mathematics, and computer science to find hidden patterns and relationships in the data. Data mining has applications in marketing, finance, manufacturing, and healthcare to gain insights from data. The data mining process involves defining the problem, preparing data, exploring and analyzing the data, building models, validating models, and deploying the best models. Issues in data mining include handling different data types, incorporating background knowledge, and protecting privacy and security. Active areas of research will continue advancing data mining techniques.

What is Data
Mining ?

Mining and discovery of new
information in terms of
patterns or
rules from vast amounts of
data.

The process of discovering meaningful new correlations, patterns and trends by sifting
through large amounts of data stored in repositoties, using pattern recognition
technologies as well as statical and methematics techniques.

Why we mine
Data ?

Commercial View Point :-
Lots of data is being collected and warehoused .
Computers have become cheaper and more powerful.
Competitive Pressure is Strong .

Scientific View Point :-
Data collected and stored at enormous speeds (GB/hour).
Traditional techniques infeasible for raw data.
Data mining may help scientists.

On what kind of
Data...?

• Relational databases
• Data warehouses
• Transactional databases
• Advanced database systems:
Object-relational
Spacial and Temporal
Time-series
Multimedia, text
WWW

What are the goals
of Data mining?

• Prediction e.g. sales volume, earthquakes
• Identification e.g. existence of genes, system
intrusions
• Classification of different categories e.g. discount
seeking shoppers or loyal regular shoppers in a
supermarket
• Optimization of limited resources such as time,
space, money or materials and maximization of
outputs such as sales or profits

What are the
applications of Data-
Mining ?

● Marketing
● Finance
 Analysis of consumer behavior
 Creditworthiness of clients
 Advertising campaigns
 Performance analysis of finance
 Targeted mailings
investments
 Segmentation of
 Fraud detection
customers, stores, or products

● Manufacturing
● Health Care
 Optimization of resources
 Discovering patterns in X-ray
 Optimization of manufacturing
images
processes
 Analyzing side effects of drugs
 Product design based on customer
 Effectiveness of treatments
requirements

What are the present
commercial tools for
Data Mining ?

Data to knowledge
SAS Oracle data-miner

Intelligent miner Clementine

How to build a data
mining model? An important concept is
that building a mining
model is part of a larger
process.

1. Defining
the
problem. Clearly define the business
problem.

2. Preparing
Data consolidate and clean the data that
was identified in the Defining the
Problem step.

3.Exploring
Data
Explore the prepared data

.

4.Building
Models Before you build a model, you must
randomly separate the prepared data into
separate training and testing datasets.
You use the training dataset to build the
model, and the testing dataset to test the
accuracy of the model by creating
prediction queries.

5. Exploring
and validating
models Explore the models that you
have built and test their
effectiveness.

6. Deploying
and updating
Deploy to a production
models environment the models
that performed the best.

What are the major
issues in Data-Mining
concept ?

 Mining different kinds of knowledge in databases
 Interactive mining of knowledge at multiple levels of
abstraction
 Incorporation of background knowledge
 Data mining query languages and ad-hoc data mining
 Expression and visualization of data mining results
 Handling noise and incomplete data
 Pattern evaluation: the interestingness problem
 Integration of the discovered knowledge with existing
knowledge: A knowledge fusion problem
 Protection of data security, integrity, and privacy

How will be the future of
Data-Mining concept?

● Active research is ongoing
 Neural Networks
 Regression Analysis
 Genetic Algorithms
● Data mining is used in many areas today. We
cannot even begin to imagine what the future
holds in its womb!

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Data mining concepts

1. Group 7

2. What is Data Mining ? Mining and discovery of new information in terms of patterns or rules from vast amounts of data. The process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositoties, using pattern recognition technologies as well as statical and methematics techniques.

3. Why we mine Data ? Commercial View Point :- Lots of data is being collected and warehoused . Computers have become cheaper and more powerful. Competitive Pressure is Strong . Scientific View Point :- Data collected and stored at enormous speeds (GB/hour). Traditional techniques infeasible for raw data. Data mining may help scientists.

4. On what kind of Data...? • Relational databases • Data warehouses • Transactional databases • Advanced database systems: Object-relational Spacial and Temporal Time-series Multimedia, text WWW

5. What are the goals of Data mining? • Prediction e.g. sales volume, earthquakes • Identification e.g. existence of genes, system intrusions • Classification of different categories e.g. discount seeking shoppers or loyal regular shoppers in a supermarket • Optimization of limited resources such as time, space, money or materials and maximization of outputs such as sales or profits

6. What are the applications of Data- Mining ? ● Marketing ● Finance  Analysis of consumer behavior  Creditworthiness of clients  Advertising campaigns  Performance analysis of finance  Targeted mailings investments  Segmentation of  Fraud detection customers, stores, or products ● Manufacturing ● Health Care  Optimization of resources  Discovering patterns in X-ray  Optimization of manufacturing images processes  Analyzing side effects of drugs  Product design based on customer  Effectiveness of treatments requirements

7. What are the present commercial tools for Data Mining ? Data to knowledge SAS Oracle data-miner Intelligent miner Clementine

8. How to build a data mining model? An important concept is that building a mining model is part of a larger process.

9. 1. Defining the problem. Clearly define the business problem.

10. 2. Preparing Data consolidate and clean the data that was identified in the Defining the Problem step.

11. 3.Exploring Data Explore the prepared data .

12. 4.Building Models Before you build a model, you must randomly separate the prepared data into separate training and testing datasets. You use the training dataset to build the model, and the testing dataset to test the accuracy of the model by creating prediction queries.

13. 5. Exploring and validating models Explore the models that you have built and test their effectiveness.

14. 6. Deploying and updating Deploy to a production models environment the models that performed the best.

15. What are the major issues in Data-Mining concept ?  Mining different kinds of knowledge in databases  Interactive mining of knowledge at multiple levels of abstraction  Incorporation of background knowledge  Data mining query languages and ad-hoc data mining  Expression and visualization of data mining results  Handling noise and incomplete data  Pattern evaluation: the interestingness problem  Integration of the discovered knowledge with existing knowledge: A knowledge fusion problem  Protection of data security, integrity, and privacy

16. How will be the future of Data-Mining concept? ● Active research is ongoing  Neural Networks  Regression Analysis  Genetic Algorithms ● Data mining is used in many areas today. We cannot even begin to imagine what the future holds in its womb!

17. Thank You !

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