mc0088 model paper

PC Training Institute Question -Bank MC0088 Data Mining (SMU) 1- MARKS QUESTIONS QS 1.

DATA

-------------

IS CONCERNED WITH FINDING HIDDEN RELATIONSHIP PRESENT IN BUSINESS

DATA TO ALLOW BUSINESS TO MAKE PREDICTIONS FOR FUTURE USE.

1. 2. 3. 4.

WAREHOUSING MINING EXTRACTION HIDING

QS 2. WHOLE LOGIG OF DATA MINING IS BASED ON MODELING. 1. 2.

TRUE FALSE

QS 3. DATA IN DATA PROCESSING IS IN DIFFERENT FORMATS ----------1. OPERATIONAL / TRANSACTIONAL DATA 2. NON-OPERATIONAL DATA 3. INFORMATION AND KNOWLEDGE 4. ALL OF THE ABOVE QS 4. DATA RETRIEVAL.

WAREHOUSING IS DEFINED AS A PROCESS OF CENTRALIZED DATA MANAGEMENT AND

1. TRUE 2. FALSE QS 5. KDD STANDS FOR ----------1. KNOWLEDGE DISCOVERY IN DATABASES 2. KNOWN DISCOVERED DATABASES 3. BOTH OF THE ABOVE 4. NONE OF THE ABOVE QS 6. ------------------ IS A TECHNOLOGY THAT IS USED TO CREATE DECISION SUPPORT OLAP AND DATA MINING ARE USED TO SOLVE DIFFERENT KINDS OF ANALYTIC PROBLEMS. 1. OLAP (ONLINE ANALYTICAL PROCESSING ) 2. OLTP(ONLINE TRANSACTION PROCESSING ) 3. KDD

SOFTWARE.

4. DATA MINING QS 7. BANKING, INSURANCE, CREDIT MARKETING, TELECOMMUNICATIONS, PHARMACEUTICALS AND BIOINFORMATICS ARE THE DIFFERENT APPLICATIONS IN THE INDUSTRY IN WHICH DATA MINING IS USED 1. CORRECT 2. INCORRECT QS 8. ----------- PROVIDES SOFTWARE CALLED DARWIN, WHICH IS DATA MINING TOOL. IT INCORPORATES CLUSTER ANALYSIS, CLASSIFICATION, AND PREDICTION AND ASSOCIATION RULES 1. 2. 3. 4.

INTELLIGENT MINER (IBM CORP) WEAK 3-A ORACLE 10 G ENTERPRISE MINER (SAS INSTITUTE INC.)

QS 9. THE

CONSTRUCTION OF DATA WAREHOUSE, WHICH INVOLVES

-------------CAN

BE VIEWED AS

AN IMPORTANT PREPROCESSING STEP FOR DATA MINING

1. 2. 3. 4.

DATA CLEANING DATA INTEGRATION, DATA HIDING BOTH 1 AND 2

QS 10. DATA WAREHOUSING PROVIDES AN INTERESTING APPROACH OF ------------- DATABASES INTEGRATION. 1. HOMOGENEOUS 2. HETEROGENEOUS 3. ALL OF THE ABOVE 4. NONE OF THE ABOVE

ALTERNATIVE TO THE TRADITIONAL

QS 11. -------------------- APPROACH REQUIRES COMPLEX INFORMATION FILTERING AND INTEGRATION PROCESSES, AND COMPETES FOR RESOURCES WITH PROCESSING AT LOCAL SOURCES. 1. 2. 3. 4.

WRAPPERS INTEGRATORS UPDATE DRIVEN QUERY DRIVEN

QS 12. DATA MODEL. 1. 2.

TRUE FALSE

WAREHOUSE AND

OLAP

TOOLS ARE NOT

BASED ON A MULTIDIMENSIONAL DATA

QS 13 ---------- WHICH CONTAINS LANGUAGE PRIMITIVES FOR DEFINING DATA WAREHOUSES AND DATA MARTS. LANGUAGE PRIMITIVES FOR SPECIFYING OTHER DATA MINING TASKS SUCH AS MINING OF CONCEPT, CLASS DESCRIPTIONS, ASSOCIATIONS, CLASSIFICATIONS AND SO ON. 1. SQL 2. DMQL 3. DATABASE LANGUAGE 4. ALL OF THE ABOVE QS 14.

TOP DOWN VIEW, DATA SOURCE VIEW

ARE THE VIEWS CONSIDERED DURING THE

1. ANALYSIS 2. TESTING 3. DESIGN 4. CONSTRUCTION QS 15. A --------- CONTAINS

, DATA WAREHOUSE VIEW AND BUSINESS -------- PHASE OF A DATA WAREHOUSE

QUERY VIEW

A SUBSET OF CORPORATE WIDE DATA THAT IS OF VALUE TO A

SPECIFIC GROUP OF USERS

1. DATA MART 2. DATA WAREHOUSE 3. DATA MINING 4. ALL OF THE ABOVE QS 16. A VIRTUAL WAREHOUSE IS A SET OF VIEWS OVER OPERATIONAL DATABASES 1. 2.

TRUE FALSE

QS 17. AGGREGATED DATA CAN BE STORED IN FACT TABLES REFERRED TO AS ----------1. FACT TABLE 2. DIMENSION TABLE 3. SUMMARY FACT TABLE 4. SUMMARY TABLE QS 18. MULTIDIMENSIONAL ANALYSIS SOFTWARE ALSO KNOWN AS -------1. OLAP 2. OLTP 3. KDD 4. MOLAP QS 19. ----------

IS INFORMATION ABOUT A COMPANY’S PAST PERFORMANCE THAT IS USED TO HELP

PREDICT THE COMPANY’S FUTURE PERFORMANCE.

1. ARTIFICIAL INTELLIGENCE (AI) 2. BUSINESS INTELLIGENCE (BI)

3. LOGICAL INTELLIGENCE 4. NONE OF THE ABOVE QS 20. TODAY’S REAL WORLD DATABASES ARE HIGHLY SUSCEPTIBLE TO -------DUE TO THEIR TYPICALLY HUGE SIZE, OFTEN SEVERAL GIGABYTES OR MORE 1. 2. 3. 4.

NOISY INCONSISTENT DATA MISSING ALL OF THE ABOVE

QS 21. BUCKETS OR BINS ARE NOT INTERCHANGEABLE TERMS 1. CORRECT 2. INCORRECT QS 22. BINNING,

CLUSTERING AND REGRESSION TECHNIQUES WORKS TO REMOVE THE NOISE FROM

THE DATA DURING TRANSFORMATION OF DATA IN

1. 2. 3. 4.

-----------

NORMALIZATION SMOOTHING AGGREGATION GENERALIZATION

QS 23. ----------

TECHNIQUES CAN BE APPLIED TO OBTAIN A REDUCED REPRESENTATION OF THE

DATA SET THAT IS MUCH SMALLER IN VOLUME YET CLOSELY CONTAINS THE INTEGRITY OF THE ORIGINAL DATA.

1. 2. 3. 4.

NORMALIZATION DATA REDUCTION SMOOTHING AGGREGATION

QS 24. SAMPLING CAN BE USED AS DATA ---------- TECHNIQUE. 1. 2. 3. 4.

NORMALIZATION CREATION REDUCTION MINING

QS 25. A

DATA MINING MEMORY AND STORAGE MANAGEMENT. 1. 2.

MAJORITY OF

TRUE FALSE

SYSTEMS DO NOT USE ANY

DBMS

AND HAVE THEIR OWN

QS 26. ASSOCIATION, CLASSIFICATION, REGRESSION, CLUSTERING AND NEURAL NETWORKS ALL ARE DATA ---------- TECHNIQUES 1. 2. 3. 4.

NORMALIZATION CREATION REDUCTION MINING

QS 27. MCFS STANDS FOR A.

MAXIMUM FREQUENT CANDIDATE SET MINIMAL FREQUENT CANDIDATE SET C. NONE OF ABOVE D. ALL OF THE ABOVE B.

QS

28. MDL. 1. 2. 3. 4.

MAXIMUM DESCRIPTION LENGTH MINIMUM DESCRIPTION LENGTH MEAN DESCRIBED LENGTH MINIMUM DESCRIBED LENGTH

29. POST PRUNING APPROACH REMOVES BRANCHES FROM A ‘FULLY GROWN’ TREE. 1. TRUE 2. FALSE QS 30. Q8 CLASSIFICATION AND PREDICTION ARE TWO FORMS OF 1. DATA ANALYSIS 2. DECISION TREE 3. A AND B 4. NONE OF THESE QS 31. DECISION TREE IS BASED ON QS

1. BOTTOM-DOWN TECHNIQUE B. TOP-DOWN TECHIQUE C. DIVIDE-AND-CONQUER MANNER D. TOP-DOWN RECURSIVE DIVIDE-AND-CONQUER MANNER QS

32. PAM STANDS FOR A. PROTOTYPE ABOVE MEDOIDS B PROTOTYPE AROUND MEANS C. PARITIONING AROUND MEDOIDS D. PARITIONING ABOVE MEANS

QS

33. A USER SESSION IS A--------- RECORD SPANNING THE ENTIRE WEB

QS

34 WEB DATA IS ----------

QS

QS

1.

STRUCTURED DATA

2.

UNSTRUCTURED DATA

3.

TEXT DATA BINARY DATA

4.

BINARY DATA

35. USER NAVIGATION ACCESSING TECHNIQUE IS ------1.

WEB STRUCTURED MINING

2.

WEB USAGE MINING

3.

WEB CONTENT MINING

4.

WEB DATA DEFINITION MINING

36. E-BANKING, SEARCH ENGINE, -----------------

ONLINE AUCTION AND WEB ADVERTISMENT ARE THE FEW

APPLICATIONS OF

1.WEB STRUCTURED

MINING

2. WEB USAGE MINING 3.WEB CONTENT MINING 4.WEB DATA DEFINITION MINING QS 37. BO (BOOKMARK ORGANIZER ) COMBINES HIERARCHICAL CLUSTERING TECHNIQUES AND USER INTERACTION TO ORGANIZE A COLLECTION OF WEB DOCUMENTS BASED ON CONCEPTUAL INFORMATION. 1. TRUE 2. FALSE QS 38. E-COMMERCE SITE WILL BE DEFINED AS ANY WEB SITE OFFERING ------

QS 39. ---------

1.

PRE-SALE SUPPORT

2.

PRODUCTS FOR SALE

3.

AFTER SALES SERVICE AND BACKUP

4.

ALL OF THE ABOVE

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

ELEMENT OF A HYPERLINKED SET OF DOCUMENTS SUCH AS THE WORLD WIDE WEB.

1.

WEB AGENT

2.

LOG FILE

3.

PAGE RANK

4.

USER PROFILE

QS 40. ----------

IS SIMPLE TEXT FILE THAT ARE AUTOMATICALLY GENERATED EVERY TIME SOMEONE

ACCESSES ONE WEBSITE.

1.

WEB AGENT

2.

LOG FILE

3.

PAGE RANK

4.

USER PROFILE

2- MARKS QUESTIONS QS 1. ----------- MAY BE DETECTED BY CLUSTERING, WHERE SIMILAR VALUES ARE ORGANIZED INTO GROUPS OR “CLUSTERS”. INTUITIVELY, VALUES THAT FALL OUTSIDE OF THE SET OF CLUSTERS MAY BE CONSIDERED -----------. 1. CLUSTERS, BINS 2. GROUPS, BUCKETS\ 3. OUTLIERS, OUTLIERS 4. ALL OF THE ABOVE QS 2. STARPROBE, WEB-BASED MULTI-USER -------- AVAILABLE FOR ACADEMIC INSTITUTIONS. ---------- PROVIDES A SET OF PARTITIONED CLUSTERING ALGORITHUM THAT TREAT THE CLUSTERING PROBLEM AS AN OPTIMIZATION PROCESS. 1. CLIENT, SOM 2. SERVER , CLUTO 3. CLIENT, CLUTO 4. SERVER, ESOM QS 3. ESOM STANDS FOR --------- AND MML STANDS FOR ---------1. EMERGENT SELF-ORGANIZING MAPS, MINIMUM MESSAGE LENGTH 2. EMERGING SELF OPERATING MEASURE, MAXIMUM MESSAGE LAST 3. EMITTED SELF ORGANIZING MEASURE, MAXIMUM MINIMUM LENGTH 4. NONE OF THE ABOVE QS 4. K-MEANS, HIERARCHICAL , AGGLOMERATIVE AND DIVISIVE ARE FOUR METHODS OF -------. AND --------IS ONE OF THE SIMPLEST UNSUPERVISED LEARNING ALGORITHMS THAT SOLVE THE WELL KNOWN CLUSTERING PROBLEM. 1. CLASSIFICATION , K-MEANS 2. PREDICTION , K-MEANS 3. CLUSTERING, K-MEANS 4. ALL ARE CORRECT QS 5. CLUSTERING MAY ALSO BE CONSIDERED AS ------------ AND CLUSTERING IS ALSO CALLED -------1. SEGMENTATION, PARTITIONS WITH SIMILAR OBJECTS 2. CLASSIFICATION, SEGMENTATION 3. PREDICTION , COMPRESSION 4. SEGMENTATION, ALL OF THE ABOVE

QS 6. (I)ASSOCIATION

RULES THAT INVOLVE TWO OR MORE DIMENSION OR PREDICATES CAN BE

REFERRED TO AS MULTIDIMENSIONAL ASSOCIATION RULE.

(II) MULTIDIMENSIONAL

ASSOCIATION RULES WITH NO REPEATED PREDICATES ARE CALLED INTER

DIMENSION ASSOCIATION RULE.

1. (I)TRUE, (II)FALSE 2. (I)TRUE, (II)TRUE 3. (I)FALSE, (II)FALSE 4. (I)FALSE, (II)TRUE QS 7. CLASSIFICATION AND PREDICTION ARE TWO FORMS OF (I) 1. DATA ANALYSIS 2.DECISION TREE 3. A AND B 4. NONE OF THESE (II) CLASSIFICATION PREDICTS A. CATEGORICAL LABELS B. PREDICTION MODELS CONTINUED VALUED FUNCTION C. A AND B D. NONE OF THESE QS 8. DECISION TREE IS BASED ON (I) 1. BOTTOM-DOWN TECHNIQUE 2 . TOP-DOWN TECHIQUE 3. DIVIDE-AND-CONQUER MANNER 4. TOP-DOWN RECURSIVE DIVIDE-AND-CONQUER MANNER

(II). RECURSIVE PARTITIONING STOPS IN DECISION TREE WHEN 1. ALL SAMPLES FOR A GIVEN NODE BELONG TO SAME CLASS. 2. THERE ARE NO REMAINING ATTRIBUTES ON WHICH SAMPLES MAY BE FURTHER PARTITIONED.

3. THERE ARE NO SAMPLES FOR THE BRANCH TEST. 4. ALL THE ABOVE.

QS 9. --------- WORKS TO REMOVE THE NOISE FROM THE DATA THAT INCLUDES TECHNIQUES LIKE BINNING , CLUSTERING AND REGRESSION. THE ------- TECHNIQUES USES ENCODING MECHANISMS TO REDUCED THE DATA SET SIZE. 1. CLUSTERING , DATA REDUCTION 2. SMOOTHING, DATA COMPRESSION 3. CLASSIFICATION, DATA PROCESSING 4. BINNING, DATA REDUCTION QS 10. OLTP AND OLAP EXPANDS AS 1. ON-LINE TRANSACTION PROCESSING , ON-LINE ANALYSIS PROCESSING 2. ON-LINE TEMPORARY PROCESSING , ON-LINE ANALYSIS PROCESSING 3.

ON-LINE TRANSACTION PROCESSING , ON-LINE ACCURATE PROCESSING

4. ON-LINE TIME PROCESSING , ON-LINE ANALYSIS PROCESSING QS 11. THE DATA WAREHOUSE VIEW INCLUDES FACT TABLE AND --------TABLE . THE

BUSINESS

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

------1. FACT , PROGRAMMER 2. DIMENSION , DEVELOPER 3. FACT, END-USER 4. ALL ARE CORRECT QS 12. THE -----

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.

---------

THE

INVOLVES THE RAPID GENERATION OF INCREASINGLY FUNCTIONAL SYSTEMS, WITH SHORT

INTERVALS BETWEEN SUCCESSIVE RELEASES

1 WATERFALL METHOD, SPIRAL METHOD 2. SPIRAL METHOD, WATERFALL METHOD 3. PROTOTYPE MODEL, SPIRAL METHOD 4. LINEAR METHOD, SPIRAL METHOD. QS13 THE

_________ DATABASE SERVER THAT DATA WAREHOUSE AND _______ TOOLS

BOTTOM TIER IS A

RELATIONAL DATABASE SYSTEM.

IS ALMOST ALWAYS A ARE BASED ON

OLTP

DATA MODEL.

1. 2. 3. 4.

WAREHOUSE, OLAP OLAP, ROLAP ROLAP,OLTP MOLAP, NONE OF THE ABOVE

QS14 NOISE IS RANDOM ERROR OR VARIANCE IN MEASURED VARIABLE. SRSWR STANDS FOR.

1. TRUE ,SIMPLE RANDOM SAMPLE WITH REPLACEMENT 2. FALSE, SIMPLE RANDOM SAMPLE WITHOUT REPLACEMENT QS17 THE DATA COMPRESSION TECHNIQUE USES ENCODING MECHANISMS TO ______ THE DATE SET SIZE. TO DEAL WITH LARGER DATA SETS, A SAMPLING METHOD, CALLED _____________ 1. REDUCE, CLARA 2. REASE, DARA 3. EQUAL, PAM 4. NONE, NONE OF THE ABOVE QS 18. (I) A MAJORITY OF DATA MINING SYSTEMS DO NOT USE ANY DBMS AND HAVE THEIR OWN MEMORY AND STORAGE MGMT. (II)DATA MINING SUPPORTS AUTOMATIC DATA EXPLORATION. 1. (I)TRUE (II) FALSE 2. (I)TRUE (II) TRUE 3. (I)FALSE (II) FALSE 4. (I)FALSE (II) TRUE QS 19. NEURAL NETWORKS, CLASSIFICATION, REGRESSION , CLUSTERING AND ASSOCIATION ARE DATA -------- TECHNIQUES, -------- MAKE USE OF EXISTING VARIABLES IN THE DATABASE IN ORDER TO PREDICT UNKNOWN OR FUTURE VALUES OF INTEREST

1. MINING, PREDICTION 2. WAREHOUSING , PREDICTION 3. MINING, DESCRIPTION 4. WAREHOUSING, DEDUCTION QS 20. (I) DATA CONSTRAINTS SPECIFY THE SET OF TASK RELEVANT DATA (II)RULE CONSTRAINTS SPECIFY THE FORM OF RULES TO BE MINED. 1. (I)TRUE (II) FALSE 2. (I)TRUE (II) TRUE 3. (I)FALSE (II) FALSE 4. (I)FALSE (II) TRUE

4-MARKS QUESTIONS QS 1. THE ENTITY RELATIONSHIP DATA MODEL IS COMMONLY USED IN THE DESIGN OF ----------WHERE A DATABASE -------- CONSISTS OF A SET OF ENTITIES AND THE RELATIONSHIPS BETWEEN THEM . ER DATA MODEL IS APPROPRIATE FOR ------- PROCESSING. A -------- REQUIRES A CONCISE SUBJECT-ORIENTED SCHEMA THAT FACILITATES ON-LINE DATA ANALYSIS. 1. RELATIONAL DATABASES, SCHEMA, ON-LINE TRANSACTION , DATAWAREHOUSE 2. HIERARCHICAL DATABASES, SCHEMA, ON-LINE TRANSACTION , DATA MINING 3. HIERARCHICAL DATABASES, SCHEMA, REAL-TIME TRANSACTION , DATA MINING 4. RELATIONAL DATABASES, SCHEMA, ON-LINE TRANSACTION , DATA CLASSIFICATION QS 2.(I) DATA WAREHOUSE AND OLAP TOOLS ARE NOT BASED ON MULTIDIMENSIONAL DATA

(II) THE DATA SOURCE VIEW EXPOSES THE INFORMATION BEING CAPTURED , STROED AND MANAGED BY OPERATIONAL SYSTEMS

(III)RELATIONAL OLAP ARE THE INTERMEDIATE SERVERS THAT STAND IN BETWEEN A RELATIONAL BACK-END SERVER AND CLIENT FRONT-END TOOLS (IV)A VIRTUAL MACHINE IS A SET OF VIEWS OVER OPERATIONAL DATABASES 1. (I)TRUE (II)TRUE(III)TRUE(IV)TRUE 2. (I)FALSE (II)TRUE(III)TRUE(IV)TRUE 3. (I)TRUE (II)TRUE(III)TRUE(IV)FALSE 4. (I)TRUE (II)TRUE(III)FALSE(IV)FALSE QS 3. ANN, FP TREE, OLTP AND OLAP 1. ARTICRAFT NEURAL NETWORK, FREQUENT PATTERN TREE, ON-LINE TEMPORARY PROCESSING , ON-LINE ANALYSIS PROCESSING 2. ARTIFICIAL NEURAL NETWORK, FREQUENT PATTERN TREE, ON-LINE TRANSACTION PROCESSING , ON-LINE ANALYSIS PROCESSING 3. ARTISTIC NEURAL NETWORK, FREQUENT PATTERN TREE, ON-LINE TEMPORARY PROCESSING , ON-LINE ANALYSIS PROCESSING 4. ARTICRAFT NEURAL NETWORK, FREQUENT PATTERN TREE, ON-LINE TEMPORARY PROCESSING , ON-LINE ANALYSIS PROCESSING QS 4. ----------- SPECIFY THE TYPE OF KNOWLEDGE TO BE MINED. DATA CONSTRAINTS SPECIFY THE SET OF ---------. DIMENSIONAL CONSTRAINTS SPECIFY THE DIMENSION OF THE ---------- AND RULE CONSTRAINTS SPECIFY THE FORM OF ------ TO BE MINED 1. KNOWLEDGE TYPE CONSTRICTS, TIME-RELATED DATA, INFORMATION, RULE 2. KNOWLEDGE TYPE CONSTRICTS, TIME-RELATED DATA, INFORNATION, RULE 3. KNOWLEDGE TYPE CONSTRICTS, TIME-RELATED DATA, DATA, INTERESTINGNESS 4. KNOWLEDGE TYPE CONSTRICTS, TASK-RELATED DATA, DATA, RULE QS 5. K-MEAN, AGGLOMERATIVE AND HIERACHICAL ARE METHODS OF -------SINGLE LINK CLUSTERING ALSO CALLED -------- COMPLETE LINK CLUSTERING ALSO CALLED AS --------- METHOD. ---------- IS USED FOR DATA MINING. 1. CLASSIFICATION, CONNECTEDNESS, DIAMETER, DATA WAREHOUSE 2. CLUSTERING, CONNECTEDNESS, AREA, CLUSTERING 3. CLUSTERING, CONNECTEDNESS, DIAMETER, CLUSTERING 4.

CLUSTERING, ISOLATED, DIAMETER, DATA MART

QS 6.(I) CLUSTERING MAY ALSO BE CONSIDERED AS SEGMENTATION . (II)SEGMENTATION, COMPRESSION , AND PARTITIONS WITH SIMILAR OBJECT ALL ARE NOT CLUSTERING METHODS

(III)CLUSTERING IS NOT USED ONLY IN DATA MINING (IV)SUPERVISED LEARNING IS REPRESENTED IN THE FORM OF CLUSTERING. 1. (I)TRUE (II)TRUE(III)TRUE(IV)TRUE 2. (I)FALSE (II)TRUE(III)TRUE(IV)TRUE 3. (I)TRUE (II)TRUE(III)TRUE(IV)FALSE 4. (I)TRUE (II)FALSE(III)FALSE(IV)TRUE

QS 7. WEB CONTENT MINING, WEB STRUCTURE MINING AND WEB USAGE MINING ALL COMES UNDER ------. AND --------- IS SIMPLE TEXT FILES THAT ARE AUTOMATICALLY GENERATED EVERY TIME SOMEONE ACCESSES ONE WEBSITE .--------- IS A LINK ANALYSIS THAT ASSIGNS A NUMERICAL WEIGHING TO EACH ELEMENT OF A HYPERLINKED SET OF DOCUMENTS SUCH AS THE WORLD WIDE WEB.

--------- A SOFTWARE AGENT IS A COMPUTER PROGRAM WHICH RUNS ON AN

AGENT INTERACTION MACHINE

1. WEB MINING, LOG FILE, PAGE RANK , WEB AGENT 2. WEB WAREHOUSING, DATA FILE, PAGE RANK , WEB AGENT 3. WEB MINING, LOG FILE, USER PROFILE , WEB AGENT 4. WEB MINING, LOG FILE, PAGE RANK , WEB MINING QS 8. ----------DATA QUALITY SOLUTION PROVIDES AN ENTERPRISE SOLUTION FOR PROFILING CLEANSING, AUGMENTING AND INTEGRATING DATA TO CREATE CONSISTENT , RELAIABLE-------, WITH SAS DATA QUALITY SOLUTION YOU CAN AUTOMATICALLY INCORPORATED DATA QUALITY INTO DATA INTEGRATION AND -----------PROJECTS TO DRAMATICALLY IMPROVE RETURNS ON YOUR ORGANIZATION ‘S -----------INITATIVES. 1. SAS ,INFORMATION, BUSINESS INTELLIEGENCE, STRATEGIES 2. GNU, INFORMATION, BUSINESS INTELLIEGENCE, STRATEGIES 3. SAS, DECISIONS, BUSINESS INTELLIEGENCE, RULES 4. GNU, DATA, BUSINESS INTELLIEGENCE, POLICIES QS 9. WEKA IS A COLLECTION OF MACHINE LEARNING ALGORITHUM FOR -------TASKS, THE ALGORITHUMS CAN EITHER BE APPLIED DIRECTLY TO A DATASET OR CALLED FROM YOUR OWN JAVA CODE.

WEKA CONTAINS TOOLS FOR DATA PREPROCESSING , CLASSIFICATION, REGRESSION, , ASSOCIATION RULES AND-----------. IT IS WELL SUITED FOR DEVELOPING NEW MACHINE LEARNING------. 1. DATA WAREHOUSING, IMAGINATION, RULES 2. DATA MINING, VISUALIZATION, SCHEMES 3. DATA MINING, CALCULATIONS, STRATEGIES 4. DATA MART, VISUALIZATION, SCHEMES QS 10. WEB LOG ANALYSIS HAS BEEN THE FOUNDATION OF ---------- ON THE WEB IN --------- UNIQUELY IDENTIFYING USERS. A LOTS OF WORKS HAVE BEEN DONE IN THE CLUSTERING

INFORMATION RETRIEVAL DATABASES INTELLIGENT AGENTS AND TOPOLOGY WHICH PROVIDES A SOUND

------------. WEB MINING IS THE APPLICATION OF -----------. DATA VISUALIZATION, DATA MINING, DATA MART CREATION, DATA MINING DATA MINING, VISUALIZATION, SCHEMES, DATA WAREHOUSING DATA WAREHOUSING, WEB MINING, WEB CONTENT MINING, DATA MINING E-COMMERCE, WEB MINING, CONTENT SEARCH, DATA WAREHOUSE

FOUNDATION FOR THE

1. 2. 3. 4.

QS 11.(I)A USER SESSION IS A CLICK STREAM RECORD SPANNING THE ENTIRE WEB . (II) WEB STRUCTURE DESCRIBES HOW A PAGE IS USED THE DATE AND TIME IT WAS ACCESSED THE IP ADDRESSES OF THE BROWSER AD PAGE REFERENCES. (III)WEB LOG FILES ARE FREQUENTLY USED IN SEQUENTIAL MINING. (IV)STRUCTURAL MINING IS USED TO EXAMINE THE STRUCTURE OF A PARTICULAR WEBSITES AND COLLATE AND ANALYZE RELATED DATA. 1. (I)TRUE (II)TRUE(III)TRUE(IV)TRUE 2. (I)FALSE (II)TRUE(III)TRUE(IV)TRUE

3. (I)TRUE (II)TRUE(III)TRUE(IV)FALSE 4. (I)TRUE (II)TRUE(III)FALSE(IV)FALSE QS 12. EOS , KDD, GDP, AND PRIM EXPANDS AS ------1. EARLY OBSERVATION SYSTEM , KNOWLEDGE DATABASE, GRAND DOMESTIC PRODUCT , PATIENT RULE INDUCTION METHOD

2. EARTH OBSERVATION SYSTEM , KNOWLEDGE DATABASE, GROSS DOMESTIC PRODUCT , PEIODIC RULE INDUCTION METHOD

3. EARTH OBSERVATION SYSTEM , KNOWLEDGE DATABASE, GROSS DOMESTIC PRODUCT , PATIENT RULE INDUCTION METHOD

4. EASY OBSERVATION SYSTEM , KNOWLEDGE DATABASE, GRAND DOMESTIC PRODUCT , PATIENT RULE INDUCTION METHOD

QS 13. INSURANCE AND DIRECT MAIL ARE TWO INDUSTRIES THAT RELY ON -------- TO MAKE PROFITABLE BUSINESS DECISIONS. TO AID DECISION MAKING ANALYSIS CONSTRUCT ----- MODELS USING WAREHOUSE DATA TO PREDICT THE OUTCOMES OF VARIETY OF DECISION ALTERNATIVES. A --------PROFILE IS A MODEL THAT PREDICTS FUTURE PURCHASING BEHAVIOUR OF AN INDIVIDUAL CUSTOMER,GIVEN HISTORICAL TRANSACTION DATA FOR BOTH THE INDIVIDUAL AND FOR THE LARGER POPULATION OF ALL OF A PARTICULAR COMPANY’S CUSTOMERS. IT IS OFTEN BENEFICIAL TO ------DATA INTO A SMALLER NUMBER OF POINTS , EASING COMPUTATIONAL REQUIREMENTS AND REDUCING THE AMOUNT OF NOISE. 1. DATA ANALYSIS, PREDICTIVE, PREDICTIVE, AGGREGATE 2. ALTERNATIVE ANALYSIS PREDICTIVE, PREDICTIVE, AGGREGATE 3. DATA ANALYSIS, CLASSIFICATION, PREDICTIVE, NOISY 4. CLUSTER ANALYSIS, PREDICTIVE, PREDICTIVE, AGGREGATE QS 14. WIS , DRG, MBA, HOLAP MEANS 1. WEIGHT ITEM SETS ,DIAGNOSIS RELATED GROUP, MEAN BASKET ANALYSIS, HYBRID OLAP 2. WEIGHTED ITEM SETS ,DIALOGUE RELATED GROUP, MARK BASKET ANALYSIS, HYBRID OLAP 3. WEIGHTED ITEM SETS ,DIAGNOSIS RELATED GROUP, MARKET BASKET ANALYSIS, HIERARCHICAL OLAP 4. WEIGHTED ITEM SETS ,DIAGNOSIS RELATED GROUP, MARKET BASKET ANALYSIS, HYBRID OLAP QS 15. DATA STORED IN MOST TEXT DATABASES ARE ---------- TEXT DATA BASES ARE ALSO CALLED AS ------------ IS THE FIRST STEP IN TEXT RETRIEVAL SYSTEM, PRECISION , RECALL AND F-SCORE ALL ARE THE MEASURES OF THE TEXT ---------- DOCUMENTS. 1. 2. 3. 4.

SEQUENCE STRUCTURED, DOCUMENT DATABASES,

TOKENIZATION, RETRIEVAL TOKENIZATION, PROCESSING SEMI STRUCTURED, DOCUMENT DATABASES, TOKENIZATION, RETRIEVAL STRUCTURED, DOCUMENT DATABASES, TOKENIZATION, FORMATTING SEMI STRUCTURED, RELATIONAL DATABASES,