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8/3/2019 42410 Data Mining Concepts and Techniques 3305
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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.
3
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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.
5
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how does it connect
January 19, 2012 Data Mining: Concepts and Techniques 6
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DATA BASE vs. DATA MININGy QUERY
-Well defined
- SQL, Xquery
y QUERY
-Poorlydefined
-no precise querylanguage
7
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Data Mining: A KDD Process
y Data mining:thecoreofknowledgediscovery process.
8
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
10
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.
13
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Data Mining: Confluence of Multiple
Disciplines
14
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
15
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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
January 19, 2012 Data Mining: Concepts and Techniques 18
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