mc0088 model paper

PC Training Institute

Question -Bank

MC0088 Data Mining (SMU)

1- M

ARKS

Q

UESTIONS

Q

S

1.

DATA

---

IS CONCERNED WITH FINDING HIDDEN RELATIONSHIP PRESENT IN BUSINESS DATATOALLOW BUSINESSTOMAKEPREDICTIONSFORFUTUREUSE

.

1.

WAREHOUSING

2.

MINING

3.

EXTRACTION

4.

HIDING

Q

S

2.

WHOLE LOGIGOFDATAMININGISBASED ONMODELING

.

1.

TRUE

2.

FALSE

Q

S

3. D

ATAINDATA PROCESSINGISINDIFFERENTFORMATS

---1. O

PERATIONAL

/ T

RANSACTIONAL DATA

2. N

ON

-O

PERATIONALDATA

3. I

NFORMATIONAND

K

NOWLEDGE

4.

ALLOF THEABOVE

Q

S

4. D

ATA WAREHOUSING IS DEFINED AS A PROCESS OF CENTRALIZED DATA MANAGEMENT AND RETRIEVAL

.

1. T

RUE

2.

FALSE

Q

S

5. KDD

STANDSFOR

---1. K

NOWLEDGEDISCOVERYINDATABASES

2.

KNOWN DISCOVEREDDATABASES

3.

BOTHOFTHEABOVE

4.

NONEOFTHEABOVE

Q

S

6. ---

IS A TECHNOLOGYTHAT IS USEDTO CREATEDECISION SUPPORT SOFTWARE

.

OLAP

ANDDATA MININGAREUSEDTOSOLVEDIFFERENT KINDSOFANALYTICPROBLEMS

.

1. OLAP (O

NLINE

A

NALYTICAL

P

ROCESSING

)

2. OLTP(O

NLINE

T

RANSACTION

P

ROCESSING

)

4. D

ATAMINING

Q

S

7. B

ANKING

, I

NSURANCE

, C

REDIT

M

ARKETING

, T

ELECOMMUNICATIONS

, P

HARMACEUTICALS AND

B

IOINFORMATICSARE THEDIFFERENTAPPLICATIONSINTHEINDUSTRY INWHICHDATAMININGISUSED

1. C

ORRECT

2. I

NCORRECT

Q

S

8. ---

PROVIDES SOFTWARE CALLED

D

ARWIN

,

WHICH IS DATA MINING TOOL

. I

T INCORPORATES

C

LUSTERANALYSIS

,

CLASSIFICATION

,

ANDPREDICTION AND

A

SSOCIATIONRULES

1. I

NTELLIGENTMINER

(IBM C

ORP

)

2. W

EAK

3-A

3. O

RACLE

10

G

4. E

NTERPRISE

M

INER

(SAS I

NSTITUTEINC

.)

Q

S

9. T

HE CONSTRUCTION OF DATA WAREHOUSE

,

WHICH INVOLVES

---

CAN BE VIEWED AS ANIMPORTANTPREPROCESSING STEPFORDATA MINING

1. D

ATACLEANING

2. D

ATAINTEGRATION

,

3. D

ATAHIDING

4. B

OTH

1

AND

2

Q

S

10. D

ATA WAREHOUSING PROVIDES AN INTERESTING ALTERNATIVE TO THE TRADITIONAL APPROACH OF

---

DATABASESINTEGRATION

.

1. H

OMOGENEOUS

2. H

ETEROGENEOUS

3.

ALLOFTHEABOVE

4.

NONEOFTHEABOVE

Q

S

11. ---

APPROACH REQUIRES COMPLEX INFORMATION FILTERING AND INTEGRATIONPROCESSES

,

ANDCOMPETES FORRESOURCESWITHPROCESSINGATLOCALSOURCES

.

1. W

RAPPERS

2. I

NTEGRATORS

3. U

PDATE DRIVEN

4. Q

UERYDRIVEN

Q

S

12. D

ATA WAREHOUSE AND

OLAP

TOOLS ARE NOT

BASED ON A MULTIDIMENSIONAL DATA MODEL

.

1.

TRUE

Q

S

13 ---

WHICH CONTAINS LANGUAGE PRIMITIVES FOR DEFINING DATA WAREHOUSES AND DATAMARTS

. L

ANGUAGE PRIMITIVES FOR SPECIFYING OTHER DATA MININGTASKSSUCH ASMINING OF CONCEPT

,

CLASSDESCRIPTIONS

,

ASSOCIATIONS

,

CLASSIFICATIONSANDSO ON

.

1. SQL

2. DMQL

3.

DATABASELANGUAGE

4.

ALLOFTHEABOVE

Q

S

14.

TOPDOWN VIEW

,

DATASOURCE VIEW

,

DATA WAREHOUSEVIEW AND BUSINESSQUERY VIEW ARE THEVIEWS CONSIDEREDDURINGTHE

---

PHASEOFADATAWAREHOUSE

1. A

NALYSIS

2. T

ESTING

3. D

ESIGN

4.

CONSTRUCTION

Q

S

15. A --- C

ONTAINS A SUBSET OF CORPORATE WIDE DATA THAT IS OF VALUE TO A SPECIFIC GROUPOFUSERS

1. D

ATA

M

ART

2. D

ATA

W

AREHOUSE

3. D

ATAMINING

4.

ALLOFTHEABOVE

Q

S

16. A

VIRTUALWAREHOUSEISASETOFVIEWSOVER OPERATIONALDATABASES

1.

TRUE

2.

FALSE

Q

S

17. A

GGREGATEDDATA CANBESTORED INFACTTABLESREFERRED TOAS

---1.

FACTTABLE

2. D

IMENSION TABLE

3. S

UMMARY FACTTABLE

4. S

UMMARYTABLE

Q

S

18. M

ULTIDIMENSIONAL

A

NALYSISSOFTWAREALSOKNOWN AS

---1. OLAP

2. OLTP

3. KDD

4. MOLAP

Q

S

19. ---

IS INFORMATION ABOUTA COMPANY

’

SPAST PERFORMANCE THAT ISUSED TO HELP PREDICTTHECOMPANY

’

SFUTUREPERFORMANCE

.

1. A

RTIFICIALINTELLIGENCE

(AI)

3. L

OGICALINTELLIGENCE

4.

NONEOFTHEABOVE

Q

S

20. T

ODAY

’

SREALWORLDDATABASESAREHIGHLY SUSCEPTIBLETO

---D

UE TOTHEIR TYPICALLYHUGESIZE

,

OFTEN SEVERALGIGABYTES ORMORE

1.

NOISY

2.

INCONSISTENTDATA

3.

MISSING

4.

ALLOF THEABOVE

Q

S

21. B

UCKETSORBINSARENOTINTERCHANGEABLE TERMS

1.

CORRECT

2. I

NCORRECT

Q

S

22. B

INNING

,

CLUSTERING AND REGRESSION TECHNIQUES WORKS TO REMOVE THE NOISE FROM THEDATA DURINGTRANSFORMATION OFDATA IN

---1. N

ORMALIZATION

2. S

MOOTHING

3. A

GGREGATION

4. G

ENERALIZATION

Q

S

23. ---

TECHNIQUES CAN BE APPLIED TO OBTAIN A REDUCED REPRESENTATION OF THE DATASETTHATISMUCHSMALLERINVOLUMEYETCLOSELYCONTAINSTHEINTEGRITYOFTHEORIGINAL DATA

.

1. N

ORMALIZATION

2. D

ATA

R

EDUCTION

3. S

MOOTHING

4. A

GGREGATION

Q

S

24. S

AMPLING CANBEUSEDASDATA

---

TECHNIQUE

.

1. N

ORMALIZATION

2. C

REATION

3. R

EDUCTION

4. M

INING

Q

S

25. A

MAJORITY OF

D

ATA

M

INING SYSTEMS DO NOT USE ANY

DBMS

AND HAVE THEIR OWN MEMORY ANDSTORAGEMANAGEMENT

.

1.

TRUE

Q

S

26. A

SSOCIATION

,

CLASSIFICATION

,

REGRESSION

,

CLUSTERINGAND

N

EURAL

N

ETWORKSALLARE

D

ATA

---

TECHNIQUES

1. N

ORMALIZATION

2. C

REATION

3. R

EDUCTION

4. M

INING

Q

S

27. M

CFS

STANDS FOR

A

. M

AXIMUM

FREQUENT CANDIDATE SET

B

. M

INIMAL

FREQUENT CANDIDATE SET

C

. N

ONE

OF ABOVE

D

. A

LL

OF THE ABOVE

QS

28.

MDL

.

1.

MAXIMUM DESCRIPTIONLENGTH

2.

MINIMUM DESCRIPTIONLENGTH

3.

MEANDESCRIBED LENGTH

4.

MINIMUM DESCRIBEDLENGTH

QS

29.

POST PRUNINGAPPROACH REMOVESBRANCHESFROMA

‘

FULLYGROWN

’

TREE

.

1.

TRUE

2.

FALSE

QS

30.

Q

8

CLASSIFICATIONANDPREDICTIONARE TWOFORMSOF

1.

DATAANALYSIS

2.

DECISION TREE

3.

AANDB

4.

NONE OFTHESE QS

31.

DECISION TREEIS BASEDON

1.

BOTTOM

-

DOWNTECHNIQUE

B

.

TOP

-

DOWNTECHIQUE

C

.

DIVIDE

-

AND

-

CONQUER MANNER

D

.

TOP

-

DOWNRECURSIVEDIVIDE

-

AND

-

CONQUERMANNER QS

32.

PAMSTANDSFOR

A

.

PROTOTYPEABOVEMEDOIDS BPROTOTYPEAROUND MEANS C

.

PARITIONINGAROUNDMEDOIDS D

.

PARITIONINGABOVEMEANS

QS

33.

AUSERSESSION ISA

---

RECORD SPANNINGTHEENTIRE WEB QS

34

WEBDATAIS

---1.

STRUCTUREDDATA

2.

UNSTRUCTUREDDATA

3.

TEXTDATA BINARYDATA

4.

BINARYDATA

QS

35.

USERNAVIGATIONACCESSINGTECHNIQUEIS

---1.

WEBSTRUCTURED

MINING

2.

WEBUSAGEMINING

3.

WEBCONTENT MINING

4.

WEBDATA DEFINITIONMINING

QS

36.

E

-

BANKING

,

SEARCH ENGINE

,

ONLINE AUCTION AND WEB ADVERTISMENT ARE THE FEW APPLICATIONS OF

1.

WEBSTRUCTURED

MINING

2.

WEBUSAGEMINING

3.

WEBCONTENT MINING

4.

WEBDATA DEFINITIONMINING

Q

S

37. BO (B

OOKMARKORGANIZER

)

COMBINESHIERARCHICALCLUSTERING TECHNIQUES ANDUSER INTERACTION TOORGANIZEACOLLECTIONOFWEBDOCUMENTS BASEDONCONCEPTUALINFORMATION

.

1.

TRUE

2.

FALSE

Q

S

38. E-

COMMERCE SITEWILLBEDEFINEDASANYWEBSITEOFFERING

---1.

PRE

-

SALESUPPORT

2.

PRODUCTS FORSALE

3.

AFTERSALES SERVICEANDBACKUP

4.

ALLOF THEABOVE

Q

S

39. ---

IS A LINK ANALYSIS ALGORITHUM THAT ASSIGNS A NUMERICAL WEIGHING TO EACH ELEMENTOFAHYPERLINKEDSETOFDOCUMENTSSUCH ASTHEWORLDWIDEWEB

.

1.

WEBAGENT

2.

LOGFILE

3.

PAGERANK

4.

USERPROFILE

Q

S

40. ---

IS SIMPLE TEXT FILE THAT ARE AUTOMATICALLYGENERATED EVERY TIME SOMEONE ACCESSES ONEWEBSITE

.

1.

WEBAGENT

3.

PAGERANK

4.

USERPROFILE

2- M

ARKS

Q

UESTIONS

Q

S

1. ---

MAY BE DETECTED BY CLUSTERING

,

WHERE SIMILAR VALUES ARE ORGANIZED INTO GROUPS OR

“

CLUSTERS

”. I

NTUITIVELY

,

VALUESTHAT FALLOUTSIDE OFTHESETOFCLUSTERS MAYBE CONSIDERED

---.

1.

CLUSTERS

, B

INS

2. G

ROUPS

, B

UCKETS

\

3. O

UTLIERS

, O

UTLIERS

4.

ALLOFTHEABOVE

Q

S

2.

STAR

P

ROBE

,

WEB

-

BASED MULTI

-

USER

---

AVAILABLE FOR ACADEMIC INSTITUTIONS

.

---

PROVIDES A SET OF PARTITIONED CLUSTERING ALGORITHUM THAT TREAT THE CLUSTERING PROBLEMASANOPTIMIZATION PROCESS

.

1. C

LIENT

, SOM

2. S

ERVER

, CLUTO

3. C

LIENT

, CLUTO

4. S

ERVER

, ESOM

Q

S

3. ESOM

STANDSFOR

---

AND

MML

STANDSFOR

---1. E

MERGENTSELF

-

ORGANIZING

M

APS

, M

INIMUM

M

ESSAGE

L

ENGTH

2. E

MERGING SELF OPERATINGMEASURE

,

MAXIMUMMESSAGE

L

AST

3. E

MITTEDSELFORGANIZINGMEASURE

, M

AXIMUM

M

INIMUM LENGTH

4.

NONE OFTHEABOVE

Q

S

4. K-

MEANS

, H

IERARCHICAL

,

AGGLOMERATIVE AND

D

IVISIVE ARE FOUR METHODS OF

---.

A

ND

---

IS ONE OFTHE SIMPLEST UNSUPERVISED LEARNING ALGORITHMS THAT

SOLVETHE WELL KNOWN CLUSTERINGPROBLEM

.

1.

CLASSIFICATION

, K-

MEANS

2. P

REDICTION

, K-

MEANS

3.

CLUSTERING

, K-

MEANS

4.

ALLARE CORRECT

Q

S

5. C

LUSTERING MAY ALSO BE CONSIDERED AS

---

AND CLUSTERING IS ALSO CALLED

---1.

SEGMENTATION

,

PARTITIONSWITH SIMILAROBJECTS

2.

CLASSIFICATION

,

SEGMENTATION

3.

PREDICTION

,

COMPRESSION

Q

S

6. (

I

)A

SSOCIATION RULES THAT INVOLVE TWO OR MORE DIMENSION OR PREDICATES CAN BE REFERRED TOASMULTIDIMENSIONALASSOCIATIONRULE

.

(

II

) M

ULTIDIMENSIONAL ASSOCIATION RULES WITH NO REPEATED PREDICATES ARE CALLED INTER DIMENSIONASSOCIATIONRULE

.

1. (

I

)

TRUE

, (

II

)

FALSE

2. (

I

)

TRUE

, (

II

)T

RUE

3. (

I

)

FALSE

, (

II

)

FALSE

4. (

I

)

FALSE

, (

II

)

TRUE

Q

S

7. C

LASSIFICATIONAND

P

REDICTIONARE TWOFORMSOF

(I) 1. D

ATA ANALYSIS

2.D

ECISION

T

REE

3. A

AND

B

4. N

ONEOFTHESE

(

II

) C

LASSIFICATIONPREDICTS

A

.

C

ATEGORICAL LABELS

B

.

P

REDICTION MODELSCONTINUEDVALUED FUNCTION C

.

A

AND

B

D

.

N

ONEOFTHESE

Q

S

8. D

ECISION TREEISBASEDON

(I) 1. B

OTTOM

-

DOWNTECHNIQUE

2 . T

OP

-

DOWNTECHIQUE

3. D

IVIDE

-

AND

-

CONQUER MANNER

4. T

OP

-

DOWNRECURSIVEDIVIDE

-

AND

-

CONQUERMANNER

(II). R

ECURSIVE

P

ARTITIONINGSTOPSIN

D

ECISION

T

REEWHEN

1. A

LL SAMPLESFORAGIVENNODE BELONGTOSAMECLASS

.

2. T

HEREARE NOREMAININGATTRIBUTESONWHICHSAMPLESMAYBE

FURTHER PARTITIONED

.

3. T

HEREARE NOSAMPLESFOR THEBRANCHTEST

.

Q

S

9. ---

WORKSTOREMOVE THENOISEFROMTHEDATATHATINCLUDESTECHNIQUESLIKE BINNING

,

CLUSTERINGANDREGRESSION

.

THE

---

TECHNIQUESUSES ENCODINGMECHANISMSTO REDUCEDTHEDATASETSIZE

.

1.

CLUSTERING

,

DATA REDUCTION

2.

SMOOTHING

,

DATACOMPRESSION

3.

CLASSIFICATION

,

DATA PROCESSING

4.

BINNING

,

DATA REDUCTION

Q

S

10. OLTP

AND

OLAP

EXPANDSAS

1.

O

N

-

LINETRANSACTIONPROCESSING

,

ON

-

LINEANALYSISPROCESSING

2.

O

N

-

LINETEMPORARY PROCESSING

,

ON

-

LINE ANALYSISPROCESSING

3.

O

N

-

LINETRANSACTIONPROCESSING

,

ON

-

LINE ACCURATEPROCESSING

4.

O

N

-

LINETIME PROCESSING

,

ON

-

LINEANALYSISPROCESSING

Q

S

11. T

HE DATA WAREHOUSE VIEW INCLUDES FACT TABLE AND

---

TABLE

. T

HE BUSINESS QUERY VIEW IS THE PERSPECTIVE OF DATA IN THE DATA WAREHOUSE FROM THE VIEWPOINT OF THE

---1. F

ACT

,

PROGRAMMER

2. D

IMENSION

,

DEVELOPER

3. F

ACT

,

END

-

USER

4.

ALL ARECORRECT

Q

S

12. T

HE

---

PERFORMS A STRUCTURED AND SYSTEMATIC ANALYSIS AT EACH STEP BEFORE PROCEEDING TO THE NEXT WHISH IS LIKE A WATERFALL

,

FALLING FROM ONE STEP TO NEXT

. T

HE

---

INVOLVES THE RAPID GENERATION OF INCREASINGLY FUNCTIONAL SYSTEMS

,

WITH SHORT INTERVALSBETWEEN SUCCESSIVERELEASES

1 W

ATERFALL METHOD

,

SPIRAL METHOD

2. S

PIRAL METHOD

,

WATERFALLMETHOD

3.

PROTOTYPEMODEL

,

SPIRAL METHOD

4. L

INEARMETHOD

,

SPIRALMETHOD

.

Q

S

13 T

HE BOTTOM TIER IS A

_________

DATABASE SERVER THAT IS ALMOST ALWAYS A RELATIONAL DATABASE SYSTEM

. D

ATA WAREHOUSE AND

_______

TOOLS ARE BASED ON

OLTP

DATAMODEL

.

1. W

AREHOUSE

, OLAP

2. OLAP, ROLAP

3. ROLAP,OLTP

4. MOLAP, N

ONEOFTHEABOVE

1. T

RUE

,S

IMPLERANDOMSAMPLE WITHREPLACEMENT

2. F

ALSE

, S

IMPLE RANDOMSAMPLEWITHOUTREPLACEMENT

Q

S

17 T

HE DATACOMPRESSIONTECHNIQUEUSESENCODINGMECHANISMS TO

______

THEDATE SETSIZE

. T

ODEALWITHLARGER DATASETS

,

ASAMPLING METHOD

,

CALLED

_____________

1. R

EDUCE

, C

LARA

2.

REASE

, D

ARA

3. E

QUAL

, P

AM

4. N

ONE

, N

ONEOFTHEABOVE

Q

S

18. (

I

) A

MAJORITYOF

D

ATAMINING SYSTEMS DONOT USE ANY

DBMS

ANDHAVE THEIR OWN MEMORY ANDSTORAGEMGMT

.

(

II

)D

ATAMININGSUPPORTSAUTOMATICDATAEXPLORATION

.

1. (

I

)T

RUE

(

II

) F

ALSE

2. (

I

)T

RUE

(

II

)

TRUE

3. (

I

)

FALSE

(

II

) F

ALSE

4. (

I

)F

ALSE

(

II

)

TRUE

Q

S

19. N

EURAL NETWORKS

,

CLASSIFICATION

,

REGRESSION

,

CLUSTERING AND ASSOCIATION ARE DATA

---

TECHNIQUES

, ---

MAKEUSE OFEXISTINGVARIABLESINTHEDATABASEINORDERTO PREDICTUNKNOWNORFUTUREVALUESOFINTEREST

1.

M

INING

, P

REDICTION

2.

W

AREHOUSING

,

PREDICTION

3.

M

INING

,

DESCRIPTION

4.

W

AREHOUSING

,

DEDUCTION

Q

S

20. (

I

) D

ATA CONSTRAINTSSPECIFYTHESETOFTASKRELEVANTDATA

(

II

)R

ULECONSTRAINTSSPECIFY THEFORMOFRULESTOBEMINED

.

1. (

I

)T

RUE

(

II

) F

ALSE

2. (

I

)T

RUE

(

II

)

TRUE

3. (

I

)

FALSE

(

II

) F

ALSE

4. (

I

)F

ALSE

(

II

)

TRUE

4-M

ARKS

Q

UESTIONS

Q

S

1. T

HE

E

NTITYRELATIONSHIPDATA MODELISCOMMONLYUSEDINTHEDESIGN OF

---W

HEREADATABASE

---

CONSISTS OFASETOFENTITIESANDTHERELATIONSHIPSBETWEEN THEM

. ER

DATAMODEL ISAPPROPRIATEFOR

---

PROCESSING

. A ---

REQUIRESACONCISE SUBJECT

-

ORIENTEDSCHEMA THATFACILITATESON

-

LINEDATA ANALYSIS

.

1.

RELATIONAL DATABASES

,

SCHEMA

,

ON

-

LINETRANSACTION

,

DATAWAREHOUSE

2. H

IERARCHICALDATABASES

,

SCHEMA

,

ON

-

LINE TRANSACTION

,

DATAMINING

3. H

IERARCHICALDATABASES

,

SCHEMA

,

REAL

-

TIMETRANSACTION

,

DATAMINING

4. R

ELATIONAL

DATABASES

,

SCHEMA

,

ON

-

LINETRANSACTION

,

DATA CLASSIFICATION

(

II

)

THEDATASOURCEVIEWEXPOSES THEINFORMATIONBEINGCAPTURED

,

STROEDANDMANAGEDBY OPERATIONAL SYSTEMS

(

III

)R

ELATIONAL

OLAP

ARE THEINTERMEDIATE SERVERSTHATSTAND INBETWEEN ARELATIONAL BACK

-

END SERVERANDCLIENT FRONT

-

ENDTOOLS

(

IV

)A

VIRTUALMACHINEISASETOFVIEWSOVEROPERATIONALDATABASES

1. (

I

)T

RUE

(

II

)T

RUE

(

III

)T

RUE

(

IV

)T

RUE

2. (

I

)

FALSE

(

II

)T

RUE

(

III

)T

RUE

(

IV

)T

RUE

3. (

I

)T

RUE

(

II

)T

RUE

(

III

)T

RUE

(

IV

)

FALSE

4. (

I

)T

RUE

(

II

)T

RUE

(

III

)F

ALSE

(

IV

)

FALSE

Q

S

3. ANN, FP

TREE

, OLTP

AND

OLAP

1. A

RTICRAFT NEURALNETWORK

, F

REQUENTPATTERNTREE

, O

N

-

LINETEMPORARY PROCESSING

,

ON

-

LINEANALYSISPROCESSING

2. A

RTIFICIALNEURAL NETWORK

, F

REQUENTPATTERNTREE

, O

N

-

LINETRANSACTION PROCESSING

,

ON

-

LINEANALYSISPROCESSING

3. A

RTISTIC

NEURALNETWORK

, F

REQUENTPATTERNTREE

, O

N

-

LINETEMPORARY

PROCESSING

,

ON

-

LINEANALYSISPROCESSING

4. A

RTICRAFT NEURALNETWORK

, F

REQUENTPATTERNTREE

, O

N

-

LINETEMPORARY PROCESSING

,

ON

-

LINEANALYSISPROCESSING

Q

S

4. ---

SPECIFY THETYPEOFKNOWLEDGETOBEMINED

. D

ATACONSTRAINTSSPECIFY THE SETOF

---. D

IMENSIONALCONSTRAINTSSPECIFY THEDIMENSION OFTHE

---

ANDRULE CONSTRAINTSSPECIFY THEFORMOF

---

TOBEMINED

1.

KNOWLEDGETYPECONSTRICTS

,

TIME

-

RELATEDDATA

,

INFORMATION

,

RULE

2.

KNOWLEDGETYPECONSTRICTS

,

TIME

-

RELATEDDATA

,

INFORNATION

,

RULE

3.

KNOWLEDGETYPECONSTRICTS

,

TIME

-

RELATEDDATA

,

DATA

,

INTERESTINGNESS

4.

KNOWLEDGE TYPECONSTRICTS

,

TASK

-

RELATEDDATA

,

DATA

,

RULE

Q

S

5.

K

-

MEAN

,

AGGLOMERATIVEANDHIERACHICALAREMETHODS OF

---

SINGLE LINKCLUSTERING ALSOCALLED

---

COMPLETELINK CLUSTERINGALSOCALLEDAS

---

METHOD

. ---

IS USEDFORDATA MINING

.

1.

CLASSIFICATION

,

CONNECTEDNESS

,

DIAMETER

,

DATAWAREHOUSE

2.

CLUSTERING

,

CONNECTEDNESS

,

AREA

,

CLUSTERING

3.

CLUSTERING

,

CONNECTEDNESS

,

DIAMETER

,

CLUSTERING

4.

CLUSTERING

,

ISOLATED

,

DIAMETER

,

DATAMART

Q

S

6.(

I

)

CLUSTERINGMAYALSOBECONSIDEREDAS

SEGMENTATION

.

(

II

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EGMENTATION

,

COMPRESSION

,

ANDPARTITIONSWITHSIMILAR OBJECTALLARE NOT CLUSTERING METHODS

(

III

)

CLUSTERING ISNOTUSEDONLYINDATA MINING

(

IV

)

SUPERVISED LEARNINGIS REPRESENTEDINTHEFORMOFCLUSTERING

.

1. (

I

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RUE

(

II

)T

RUE

(

III

)T

RUE

(

IV

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RUE

2. (

I

)

FALSE

(

II

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RUE

(

III

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RUE

(

IV

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3. (

I

)T

RUE

(

II

)T

RUE

(

III

)T

RUE

(

IV

)

FALSE

Q

S

7.

WEBCONTENTMINING

,

WEBSTRUCTUREMININGANDWEBUSAGE MININGALLCOMES UNDER

---. A

ND

---

IS SIMPLETEXT FILESTHATARE AUTOMATICALLYGENERATEDEVERY TIME SOMEONEACCESSES ONEWEBSITE

.---

ISALINKANALYSISTHATASSIGNSA

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. ---

ASOFTWAREAGENTIS ACOMPUTERPROGRAM WHICHRUNSONAN AGENTINTERACTIONMACHINE

1.

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,

LOGFILE

,

PAGERANK

,

WEBAGENT

2.

WEBWAREHOUSING

,

DATAFILE

,

PAGERANK

,

WEBAGENT

3.

WEBMINING

,

LOGFILE

,

USERPROFILE

,

WEBAGENT

4.

WEBMINING

,

LOGFILE

,

PAGERANK

,

WEBMINING

Q

S

8. ---

DATAQUALITYSOLUTIONPROVIDESANENTERPRISESOLUTIONFORPROFILING CLEANSING

,

AUGMENTINGANDINTEGRATINGDATA TOCREATECONSISTENT

,

RELAIABLE

---,

WITH

S

ASDATA QUALITYSOLUTIONYOUCANAUTOMATICALLYINCORPORATEDDATAQUALITYINTO DATAINTEGRATIONAND

---

PROJECTSTO DRAMATICALLYIMPROVERETURNS ONYOUR ORGANIZATION

‘

S

---

INITATIVES

.

1. SAS ,

INFORMATION

,

BUSINESSINTELLIEGENCE

,

STRATEGIES

2. GNU,

INFORMATION

,

BUSINESSINTELLIEGENCE

,

STRATEGIES

3. SAS,

DECISIONS

,

BUSINESSINTELLIEGENCE

,

RULES

4. GNU,

DATA

,

BUSINESSINTELLIEGENCE

,

POLICIES

Q

S

9. W

EKAISACOLLECTIONOFMACHINELEARNINGALGORITHUMFOR

---

TASKS

,

THE ALGORITHUMSCANEITHER BEAPPLIEDDIRECTLYTO ADATASETORCALLEDFROMYOUR OWNJAVA CODE

. W

EKACONTAINSTOOLS FORDATAPREPROCESSING

,

CLASSIFICATION

,

REGRESSION

,

CLUSTERING

,

ASSOCIATIONRULESAND

---. I

T ISWELLSUITED FORDEVELOPINGNEWMACHINE LEARNING

---.

1.

DATAWAREHOUSING

,

IMAGINATION

,

RULES

2.

DATAMINING

,

VISUALIZATION

,

SCHEMES

3.

DATAMINING

,

CALCULATIONS

,

STRATEGIES

4.

DATAMART

,

VISUALIZATION

,

SCHEMES

Q

S

10.

WEBLOGANALYSISHAS BEENTHEFOUNDATIONOF

---

ONTHEWEB

I

N

---

UNIQUELYIDENTIFYINGUSERS

. A

LOTSOFWORKSHAVEBEENDONEINTHE

INFORMATIONRETRIEVALDATABASESINTELLIGENT AGENTSANDTOPOLOGYWHICHPROVIDESASOUND FOUNDATION FORTHE

---. W

EBMININGISTHEAPPLICATIONOF

---.

1.

DATAVISUALIZATION

,

DATAMINING

,

DATA MARTCREATION

,

DATAMINING

2.

DATAMINING

,

VISUALIZATION

,

SCHEMES

,

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3.

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,

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,

WEBCONTENTMINING

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COMMERCE

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WEBMINING

,

CONTENT SEARCH

,

DATAWAREHOUSE

Q

S

11.(

I

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USER SESSIONIS ACLICKSTREAM RECORDSPANNINGTHEENTIREWEB

.

(

II

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WEBSTRUCTUREDESCRIBES HOWAPAGEIS USEDTHEDATEANDTIMEIT WASACCESSEDTHE

IP

ADDRESSESOFTHEBROWSERADPAGEREFERENCES

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(

III

)

WEBLOGFILESARE FREQUENTLYUSEDINSEQUENTIALMINING

.

(

IV

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TRUCTURALMININGISUSEDTOEXAMINETHESTRUCTUREOFAPARTICULARWEBSITES AND COLLATEANDANALYZERELATEDDATA

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1. (

I

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RUE

(

II

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RUE

(

III

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RUE

(

IV

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RUE

3. (

I

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RUE

(

II

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RUE

(

III

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RUE

(

IV

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FALSE

4. (

I

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(

II

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RUE

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III

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ALSE

(

IV

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FALSE

Q

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12. EOS , KDD, GDP,

AND

PRIM

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ARLYOBSERVATIONSYSTEM

, K

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ARTHOBSERVATIONSYSTEM

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NOWLEDGEDATABASE

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ROSSDOMESTICPRODUCT

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PATIENTRULEINDUCTIONMETHOD

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ASYOBSERVATIONSYSTEM

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NOWLEDGE DATABASE

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RANDDOMESTIC PRODUCT

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PATIENTRULEINDUCTIONMETHOD

Q

S

13. I

NSURANCE ANDDIRECTMAILARETWOINDUSTRIESTHATRELYON

---

TO MAKE PROFITABLEBUSINESSDECISIONS

. T

OAIDDECISION MAKINGANALYSISCONSTRUCT

---

MODELS USING WAREHOUSEDATATO PREDICTTHEOUTCOMES OFVARIETYOFDECISIONALTERNATIVES

. A

---

PROFILEISAMODELTHATPREDICTSFUTUREPURCHASINGBEHAVIOUROFANINDIVIDUAL CUSTOMER

,

GIVENHISTORICALTRANSACTIONDATAFORBOTHTHEINDIVIDUAL ANDFORTHELARGER POPULATION OFALLOFAPARTICULARCOMPANY

’

SCUSTOMERS

. I

T ISOFTENBENEFICIALTO

---D

ATAINTO ASMALLER NUMBEROFPOINTS

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.

1.

DATAANALYSIS

,

PREDICTIVE

,

PREDICTIVE

,

AGGREGATE

2.

ALTERNATIVEANALYSISPREDICTIVE

,

PREDICTIVE

,

AGGREGATE

3.

DATAANALYSIS

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PREDICTIVE

,

NOISY

4.

CLUSTER ANALYSIS

,

PREDICTIVE

,

PREDICTIVE

,

AGGREGATE

Q

S

14. WIS , DRG, MBA, HOLAP

MEANS

1.

WEIGHT ITEMSETS

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IAGNOSISRELATEDGROUP

, M

EAN

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A

NALYSIS

, H

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2.

WEIGHTEDITEMSETS

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, M

ARK BASKET

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, H

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3.

WEIGHTEDITEMSETS

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, M

ARKET BASKET

A

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,

H

IERARCHICAL

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4.

WEIGHTEDITEMSETS

,D

IAGNOSISRELATEDGROUP

, M

ARKETBASKET

A

NALYSIS

, H

YBRID

OLAP

Q

S

15.

DATASTOREDINMOSTTEXT DATABASESARE

---

TEXT DATABASES AREALSOCALLED AS

---

ISTHEFIRSTSTEPINTEXT RETRIEVALSYSTEM

,

PRECISION

,

RECALLAND

F-

SCOREALLARE THEMEASURES OFTHETEXT

---

DOCUMENTS

.

1.

SEQUENCE STRUCTURED

,

DOCUMENT DATABASES

, T

OKENIZATION

,

RETRIEVAL

2.

SEMISTRUCTURED

,

RELATIONAL

DATABASES

, T

OKENIZATION

,

PROCESSING

3.

SEMISTRUCTURED

,

DOCUMENTDATABASES

, T

OKENIZATION

,

RETRIEVAL