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By POOJA DHANDA

MCA 2NDYEAR

410104

1

Internet access via tv cable

network

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y

Introductiony How accessinternetusingcabletvnetwork

y Cable modem

y Connection

y Workofcable modem

y Isolationoftv& pc

y Ethernetovercoaxial adapter

y Hybrid accesssystem

y

Advantages & disadvantages 2

Contents:

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INTRODUCTION

y Internet data can betransferredthroughcablenetworkswiredtothe

usercomputer

y

Accessingtheinternet at 10 megabits persecond.

y Acable modem connectsto a pcusingthesameco-axial cablethat

brings all channelsto yourtelevision

y India has a cable penetrationof80 millionhomes,offering avast

networkfor leveragingtheinternet access.

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How access the internet using

the cable networky Usethedial-up telephoneservices provided by your

cablecompanyinconjunctionwith a modem orISDN

adapter.y Touse a Cable Modem.

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Cable Modem

y A Cable Modem that allowshigh-speed accesstotheinternetvia a cabletv network.

y Thiscable modem attachesto computerthrough anEthernet NetworkInterface Card.

y Ittakes a signal from thecomputer andconvertitfortransmissionoverthecablenetwork.

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how does it connect

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DATA BASE vs. DATA MININGy QUERY

-Well defined

- SQL, Xquery

y QUERY

-Poorlydefined

-no precise querylanguage

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Data Mining: A KDD Process

y Data mining:thecoreofknowledgediscovery process.

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Data Cleaning

Data Integration

Databases

Data Warehouse

Task-relevant Data

Selection

Data Mining

Pattern Evaluation

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Steps of a KDD Process

y Learningthe applicationdomainy relevant priorknowledge andgoalsofapplication

y Creating a targetdata set:data selection

y Data cleaning and preprocessing: (maytake 60% ofeffort)y Data reduction andtransformation:

y Finduseful features,dimensionality/variablereduction,invariantrepresentation.

y Choosingfunctionsofdata miningy

summarization,classification,regression, association,clustering.y Choosingthe mining algorithm(s)

y Data mining:searchfor patternsofinterest

y Patternevaluation andknowledge presentationy visualization,transformation,removingredundant patterns,etc.

y Useofdiscoveredknowledge9

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Data Mining and Business

Intelligence

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Increasing potential

to support

business decisions End User

BusinessAnalyst

Data

Analyst

DBA

Making

Decisions

Data Presentation

Visualization Techniques

Data Mining

Information Discovery

Data Exploration

OLAP, MDA

Statistical Analysis, Querying and Reporting

Data Warehouses / Data Marts

Data SourcesPaper, Files, Information Providers, Database Systems, OLTP

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Data Mining: On What Kind of Data?

y Relational databases

y Datawarehouses

y Transactional databasey Spatial andtemporal data

multimedia databases

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Are All the Discovered Patterns

Interesting?

y Adata miningsystem/query maygeneratethousandsofpatterns,not

all ofthem areinteresting.

y Interestingness measures: A patternisinteresting ifitiseasily

understood byhumans,validonnewortestdatawithsomedegreeof

certainty, potentiallyuseful,novel,orvalidatessomehypothesis that a

userseekstoconfirm

y Objective vs. subjective interestingness measures:

y Objective: basedonstatistics andstructuresofpatterns,e.g.,support,

confidence,etc.

y Subjective: basedonusers beliefinthedata,e.g.,unexpectedness,novelty,

actionability,etc.

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Data Mining: Confluence of Multiple

Disciplines

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Data Mining

DatabaseTechnology

Statistics

OtherDisciplines

InformationScience

MachineLearning

Visualization

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Major Issues in Data Mining (1)

y Mining methodology anduserinteraction

y Miningdifferentkindsofknowledgeindatabases

y

Incorporationofbackgroundknowledgey Data mining query languages.

y Expression andvisualizationofdata miningresults

y Handlingnoise andincompletedata

y Patternevaluation:theinterestingness problem

y Performance andscalability

y Efficiency andscalabilityofdata mining algorithms

y Parallel,distributed andincremental mining methods

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Major Issues in Data Mining (2)

y Issuesrelatingtothediversityofdata typesy Handlingrelational andcomplex typesofdata

y

Mininginformationfrom heterogeneousdatabases andglobalinformationsystems (WWW)

y Issuesrelatedto applications andsocial impactsy Applicationofdiscoveredknowledge

y Domain-specificdata miningtools

y Intelligent query answering

y Processcontrol anddecision making

y Integrationofthediscoveredknowledgewithexistingknowledge: Aknowledgefusion problem

y Protectionofdata security,integrity, and privacy16

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Summary

y Data mining:discoveringinteresting patternsfrom large amountsof

data

y Anatural evolutionofdatabasetechnology,ingreatdemand,withwide

applications

y A KDD processincludesdata cleaning,data integration,data selection,

transformation,data mining, patternevaluation, andknowledge

presentation

y Miningcan be performedin avarietyofinformationrepositories

y Classificationofdata miningsystems

y Majorissuesindata mining

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Thank you

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