data mining & warehousing- presentation

7/30/2019 Data Mining & Warehousing- Presentation

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NADEEM . A 111A1012

JAYADURGA .S 111A1009

RAGINI .S 111A1013


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WHICH ARE OURLOWEST/HIGHESTMARGIN

CUSTOMERS ?

WHO ARE MY CUSTOMERSAND WHAT PRODUCTS

ARE THEY BUYING?

WHICH CUSTOMERS

ARE MOST LIKELY TOGO TO THECOMPETITORS ?

WHAT IMPACT WILLNEW PRODUCTS ORSERVICESHAVE ON REVENUE

AND MARGINS?

WHAT PRODUCT

PROMOTIONSHAVE THE BIGGESTIMPACT ON REVENUE?

WHAT IS THE MOSTEFFECTIVEDISTRIBUTION

CHANNEL?


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I CANT GET THE DATA I NEED NEED AN EXPERT TO GET THE DATA

I CANT UNDERSTAND THE

DATA I FOUND

AVAILABLE DATA POORLYDOCUMENTED

I CANT USE THE DATA I FOUND RESULTS ARE UNEXPECTED DATA NEEDS TO BE TRANSFORMED FROM

ONE FORM TO OTHER

I CANT FIND THE DATA I NEED DATA IS SCATTERED OVER THE NETWORK

MANY VERSIONS, SUBTLE DIFFERENCES


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1960S: DATA COLLECTION, DATABASE CREATION, IMS AND

NETWORK DBMS

1970S: RELATIONAL DATA MODEL, RELATIONAL DBMS

IMPLEMENTATION

1980S: RDBMS, ADVANCED DATA MODELS (EXTENDED-

RELATIONAL, OO, DEDUCTIVE, ETC.) AND APPLICATION-ORIENTED DBMS (SPATIAL, SCIENTIFIC, ENGINEERING,ETC.)

1990S2000S: DATA MINING AND DATA WAREHOUSING, MULTIMEDIA

DATABASES, AND WEB DATABASES

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The data warehouse is that portion of anoverall Architected Data Environment thatserves as the single integrated source of

data for processing information.


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Data explosion problem

Automated data collection tools and mature database

technology lead to tremendous amounts of data stored

in databases, data warehouses and other information

repositories

We are drowning in data, but starving for knowledge!

Solution: Data warehousing and data mining

Extraction of interesting knowledge (rules, regularities,

patterns, constraints) from data in large databases

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DATA

WAREHOUSE

SubjectOriented

Integrated

NonVolatile

Time

variant

Accessible

Process

Oriented


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DATA MART

STAGING AREA

OLAP

OLAP TOOLS


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Data

Acquisition

Warehouse

Design

Analytical

Data Store

Enterprise

Warehouse

Data Marts

Metadata

Directory

Metadata

Repository

DA

TA

MANAG

EMENT

M

ETADATA

MA

NAGEMENT

Data

Analysis

Web

Information

Systems

Operational,

External &otherDatabases


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The data warehouse is distinctly different from theoperational data used and maintained by day-to-day operational systems. Data warehousing is notsimply an access wrapper for operational data,where data is simply dumped into tables for

direct access.


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OPERATIONAL DATA

Application oriented

Detailed

Accurate, as of the moment ofaccess

Serves the clerical community

Performance sensitive

(immediate response required

when entering a transaction)Flexible structure; variable

contents

Small amount of data used in aprocess

DATA WAREHOUSE

Subject oriented

Summarized

Represents values over time

Serves the managerial

community

Performance relaxed(immediacy not required)

Static structure

large amount of data used in aprocess


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Data mining (knowledge discovery indatabases): Extraction of interesting (non-trivial, implicit,

previously unknown and potentially useful)information or patterns from data in largedatabases

Process of finding different patterns or co-relationsamong the data in large relational databases.

Popular and highly used in the INFORMATIONINDUSTRY

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Market Analysis And Management

Corporate Analysis And RiskManagement

Fraud Detection And Management

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Where are the data sources for analysis? Credit card transactions, loyalty cards, discount coupons,

customer complaint calls, plus (public) lifestyle studies

Target marketing Find clusters of model customers who share the same

characteristics: interest, income level, spending habits, etc.

Determine customer purchasing patterns over time

Conversion of single to a joint bank account: marriage, etc. Cross-market analysis

Associations/co-relations between product sales

Prediction based on the association information

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Customer profiling

Data mining can tell you what types of customers buywhat products (clustering or classification)

Identifying customer requirements

Identifying the best products for different customers

Use prediction to find what factors will attract newcustomers

Provides summary information

Various multidimensional summary reports

Statistical summary information (data central tendencyand variation)


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Finance planning and asset evaluation Cash flow analysis and predictionAnalysis of trends, financial ratios and market value

Resource planning: Summarize and compare the resources and spending

Competition: Monitor competitors and market directions

Group customers into classes and a class-based pricingprocedure Set pricing strategy in a highly competitive market

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Applications widely used in health care, retail, credit card services,

telecommunications (phone card fraud), etc.

Approach use historical data to build models of fraudulent behavior

and use data mining to help identify similar instances

Examples auto insurance: detect a group of people who stage

accidents to collect on insurance money laundering: detect suspicious money transactions

(US Treasury's Financial Crimes Enforcement Network) medical insurance: detect professional patients and ring

of doctors and ring of references20


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Detecting inappropriate medical treatmentAustralian Health Insurance Commission identifies that in

many cases blanket screening tests were requested (saveAustralian $1m/yr.).

Detecting telephone fraud Telephone call model: destination of the call, duration,

time of day or week. Analyze patterns that deviate froman expected norm.

British Telecom identified discrete groups of callers withfrequent intra-group calls, especially mobile phones, andbroke a multimillion dollar fraud.


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Sports IBM Advanced Scout analyzed NBA game statistics

(shots blocked, assists, and fouls) to gain competitiveadvantage for New York Knicks and Miami Heat

AstronomyJPL and the Palomar Observatory discovered 22 quasars

with the help of data mining

Internet Web Surf-Aid IBM Surf-Aid applies data mining algorithms to Web

access logs for market-related pages to discovercustomer preference and behavior pages, analyzingeffectiveness of Web marketing, improving Web siteorganization, etc.

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Other Applications

Text mining (news group, email,documents)

Stream data mining

Web mining.

DNA data analysis


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data mining & warehousing- presentation

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