unit- 3 data mining pt.pptx

7/28/2019 UNIT- 3 data mining pt.pptx

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UNIT- 3

DATA MINING


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WHAT IS DATA MINING?

Data Mining is a collection of techniques forefficient automated discovery of previouslyunknown , valid , novel, useful andunderstandable patterns in large databases.

Pattern must be actionable so that they maybe used in an enterprises decision makingprocess

It is also known as Knowledge Discovery

Data Mining refer to the extraction of hiddenpredictive information patterns from largedatabase.


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DATA MINING

Raw

Information

Hidden

information

PatternData Mining


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NEED FOR DATA MINING

Data mining has found many application in thelast few years for a number of reasons:

Growth in OLTP data

Growth in data due to cards Growth in data due to web

Growth in data due to telephone transactions,banking, medical.

Growth in data storage capacity Decline in cost processing

Availability of software/ tool


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DATA MINING PROCESS

Requirement analysis

Clearly define goals

Clearly define business problem

Data Selection and Collection

Cleaning and preparing data

Data mining exploration and validation

Implementing , evaluating and monitoring

Results visualization


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CRISP( CROSS INDUSTRY STANDARD PROCESS)

DATA MINING MODEL


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KNOWLEDGE DISCOVERY PROCESS

Data mining is process of knowledge

discovery.

Data mining discovers knowledge or

information that you never knew was present

in your data.

Knowledge itself as manifests itself as

relationships and patterns.


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RELATIONSHIPS

Data mining discovers relationships between

two or more different objects along with the

time dimension.

Sometime relationship may occur between

same objects

Discovery of relationships is a key result of

data mining.


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KNOWLEDGE DISCOVERY PHASES

1. Define Business Objectives

2. Prepare data

3.

Perform data mining4. Evaluate results

5. Present Discoveries

6. Incorporate Usage of Discoveries


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KNOWLEDGE DISCOVERY PROCESS

Determination

Of business objectives

Evaluation and

Application of

Results

Application of suitable DataMining Techniques

Selection and

preparation of data


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DATA MINING VS DATA WAREHOUSE

OLAP

What is happening inenterprise.

Summary data

Limited dimensions

Small number of attributes.

User driven , interactive

analysis Multidimensional , drill

down , and slice- and- dice

Mature and widely used

DATA MINING

Predict future based onwhy this happening.

Detailed transaction- level

data. Large dimensions

Many dimension attributes.

Data- driven automatic

knowledge discovery. Prepare data, mining tools

Still emerging


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RELATIONSHIP OF DATA WAREHOUSE AND

DATA MINING Data mining algorithms need large amount of

data, detailed level data whereas in datawarehouse contain lowest level of data.

Data mining need integrated and cleansed datawhereas data warehouse contain data that issuitable for data mining.

Infrastructure of data warehouse is robust, withparallel processing technology and relationaldatabase systems since data mining needs thistype of data


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DATA MINING TECHNIQUES

Association rules mining or market basket

analysis

Supervised classification

Cluster Analysis

Web data mining

Search engines


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TECHNIQUES

Association rules mining or market

basket analysis

Transaction Items bought

1

2

3

4

bread, milk, cheese

bread, cheese

jam, milk

milk, ghee

Now here we can see maximum combination of bread and cheese


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SUPERVISED CLASSIFICATION

Data mining technique origin from machinelearning techniques.

It help in predicting whether an individual is

likely to respond to a direct mail or not. Identify good risk for granting loans or

insurance.

Rule for insurance

If sex= female & 19


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CLUSTER ANALYSIS

Grouping data into disjoint sets that are similar in

some respect. It also attempts to place dissimilar

data in different clusters.

For example, in the context of super market data,clustering of sale items to perform effective shelf

space organization is a typical application


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WEB DATA MINING

It has impact on way we search &findinformation at home and at work

Evaluation of learning Sites

Example :- student portalCheck login

Notes

Submit online testChat page for clarifying doubts


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SEARCH ENGINES

It is huge databases of web pages and

software package for indexing and retrieving

pages that enable users to find information

Ranking help the user to choose best one


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DATA MINING APPLICATION

Customer Segmentation

Market basket analysis

Risk management Fraud detection

Demand prediction

Delinquency Tracking

unit- 3 data mining pt.pptx

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