Course File Leaf (Theory) For the Academic Year (Odd/Even Semester)

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Nadar Saraswathi College of Engineering and Technology, Vadapudupatti, Theni - 625 531 (Approved by AICTE, New Delhi and Affiliated to Anna University, Chennai) Course File Leaf (Theory) For the

E/CSE Year / Semester III/VI Credit 03 Subject Code/Choice IT6702/Core Subject Name Data warehousing and data mining S. No. CONTENTS S. No. CONTENTS 1. Syllabus 14. Tutorial Sheet(If Applicable) 2.

1 Nadar Saraswathi College of Engineering and Technology, Vadapudupatti, Theni (Approved by AICTE, New Delhi and Affiliated to Anna University, Chennai) Course File Leaf (Theory) For the Academic Year (Odd/Even Semester) Format No. Rev. No. 02 NAC/TLP-07a Date Total Page 01 Staff Name A.DuraiMurugan Dept. / Designation AP/CSE Strength 35 Course/Branch B.E/CSE Year / Semester III/VI Credit 03 Subject Code/Choice IT6702/Core Subject Name Data warehousing and data mining S. No. CONTENTS S. No. CONTENTS 1. Syllabus 14. Tutorial Sheet(If Applicable) 2. Course plan 15. Assignments Information (Group & Individual ) with Samples 3. Students Name list 16. Evaluation Documents (GD, RP, Etc.,) 4. Individual Time Table 17. CAT / Unit Test Questions 5. Course Materials-Unit I 18. CAT / Unit Test Mark Statement & Analysis 6. Course Materials-Unit II 19. Internal Questions and Answer Key 7. Course Materials-Unit III 20. Internal Mark Statement 8. Course Materials-Unit IV 21. Internal Result Analysis Report 9. Course Materials-Unit V Soft copy study materials (CDs) 11. Exam Cell Question Bank (Part-A,B&C) 12. Question Bank (Two marks with answers) 13. Working Models / Project / Paper (CDs, PPTs) Internal Answer papers ( Best, Average, Poor) 23. University Questions and Answer Key 24. University Feedback Summary (with Question) 25. University Mark Statement 26. University Result Analysis Review Particulars: No. Date Done by Remarks Signature * (Verified By Department Audit In charge) Staff Alteration (If any): from. Staff In Charge Head of the Department Principal

2 I. Instructional Objective (5) : The student should be made to : IO1: Understand the concept of building a data warehouse and to analyze the mapping concepts. IO2: Be familiar with the OLAP tools, application and its categories of application. IO3: Understand the concept of data mining techniques, process and about its query languages. IO4: Analyze the various mining association rules and understand the concept of classifications. IO5: Be familiar with the cluster analysis and categorization of clustering methods. II. Pre requisites : Nadar Saraswathi College of Engineering and Technology, Vadapudupatti, Theni (Approved by AICTE, New Delhi and Affiliated to Anna University, Chennai) Course Plan (Theory) For the Academic Year (Odd/Even Semester) Student should know about the Relational database normalization techniques. Student should know about the Physical design of a database. Student should know the concepts of algorithm design and analysis of mining techniques. III. Guidelines : Database management systems and the Fundamentals of database. IV. Course Out Come (5) : At the end of this course, the students will be able to CO1: Have knowledge of building data warehouse, its schemas and mapping techniques. CO2: Know about Online analytical tools and its categories. CO3: Have knowledge of data mining, processing, cleaning and the concepts of query languages. CO4: Have knowledge of association rules and its classification. CO5: know about the cluster analysis and major clustering methods. PO - CO Correlation Matrix: (3- > = 80%, 2- > = 60%, 1 > = 40%) Format No. Rev. No. 01 NAC/TLP-07a.2 Date Total Pages 05 Staff Name A.DuraiMurugan Dept. / Designation CSE/AP Strength 35 Course/Branch B.E/CSE Year / Semester III/VI Credit 03 Subject Code/Choice IT6702/Core/Elective Subject Name Data warehousing and data mining. PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 a b c d e f g h i j k l CO CO CO CO CO V. Books to be Referred : T1: Alex Berson & Stephen J.Smith, Data Warehousing, Data Mining and OLAP, Tata McGraw Hill Edition, Thirteenth Reprint R1: Dr.S.Poonkuzhali & B.S.Charulatha, Data Warehousing and Data Mining, Revised Edition, Charulatha Publication,2016.

3 VI. E-Learning Resources : EL1: EL2: nptel.ac.in/courses/ /35 EL3: EL4: VII. VIII. Method of Evaluation : (Attach Samples) 6.1 Internal Assessment Examination (INT1, INT2, and MOD1/2): [ ] 6.2 Unit Test : [ ] / CAT Test : [ ] 6.3 Assignments / Seminars : [ ] 6.4 Mini Projects : [ ] 6.5 Quiz : [ ] 6.6 Pre Semester Examinations : [ ] 6.7 Others : [ ] Group Discussion / Role Play / Course Exit Survey Lesson Plan: S. No. Topic Introduction about Data mining and Data ware house components. Building a data ware house a. Business considerations. b. Design considerations. Building a data ware house c. Technical considerations. d. Implementation consideration IOs / POs Correlation Content Delivery Mech* Reference Book no. & Page no. UNIT I - DATA WAREHOUSING No. of Periods Required IO1/a BB R IO1/a,b,c,e BB R1 1.8/1.13 IO1/a,b,c,e BB R1 1.14/1.17 Cumulative Periods Mapping the data ware house. IO1/e BB R1 1.22/ Parallel Rdbms and Vendors. IO1/a,b,e BB R1 1.30/ DBMS Schemas. IO1/b,e BB R1 1.35/ Star Schema, Star join and Star index. IO1/b,e PPT R1 1.37/ Data Extraction, clean up and tools. IO1/c,b,e BB R1 1.43/ Metadata. IO1/b,c BB R1 1.57/1.64 UNIT-II- BUSINESS ANALYSIS Introduction About Reporting and Query tools. IO1/e BB R1 2.1/ Tool Categories. IO1/e BB R1 2.2/

4 12. Cognos Impromptu. IO1/e BB R1 2.4/ Application. IO1/b,c,e BB R1 2.9/2.18 Online Analytical Processing. 14. a. Need of OLAP. IO1/e BB R1 b. Guidelines. 2.21/2.24 Online Analytical Processing. 15. c. Classification of Tools. IO1/e BB R1 d. Products. 2.24/2.28 UNIT-III- DATA MINING Introduction and What kind of Data. IO1/a BB R1 3.1/ Data mining Functionalities. IO1/a PPT R1 3.8/ Classification and Issues. IO1/a BB R1 3.11/ Data Pre-processing. IO1/a,b BB R1 3.17/ Data Cleaning. IO1/a,b BB R1 3.19/ Data integration, transformation and primitives IO1/a,b BB R1 3.23/ Data Reduction IO1/a,b BB R1 3.28/ Data Discretization. IO1/a,b BB R1 3.38/ Data mining Query languages. IO1/a,b,c BB R1 3.45/3.52 UNIT-IV- ASSOCIATION RULE MINING AND CLASSIFICATION Introduction- Association rule mining a. Transactional databases. IO1/a,b,c,d BB R1 4.1/ Multi-level Association Rules. IO1/ a,b,c,d BB R1 4.28/ Issues regarding classification and prediction IO1/ a,b,c,d BB R1 4.33/ Decision tree induction IO1/ a,b,c,d BB R1 4.34/ Bayesian Classifiaction IO1/ a,b,c,d BB R1 4.58/

5 30. Classification of Back propagation. IO1/ a,b,c,d BB R1 4.68/ Classification methods. IO1/ a,b,c,d BB R1 4.72/ Prediction. IO1/ a,b,c,d BB R1 4.76/ Classifier Accuracy. IO1/ a,b,c,d BB R1 4.80/ UNIT-V- CLUSTERING AND APPLICATIONS AND TRENDS IN DATA MINING 34. Introduction-Cluster analysis IO1/ a,b,c,d BB R1 5.1/ Types of Data in cluster and categorization. IO1/ a,b,c,d BB R1 5.2/ Partition and Hierarchical methods. IO1/ a,b,c,d PPT R1 5.12/ Density and grid based clustering Methods. IO1/ a,b,c,d BB R1 5.35/ Model based and Outlier analysis. IO1/ a,b,c,d BB R1 5.43/ Spatial and multimedia data mining. IO1/ a,b,c,d BB R1 5.51/ Mining time-series and text databases. IO1/ a,b,c,d BB R1 5.59/ Mining WWW and applications. IO1/ a,b,c,d BB R1 5.73/ Social Impacts of data mining. IO1/ a,b,c,d BB R1 5.84/ * BB - Blackboard, VD Videos, GD Group Discussion, RP Role Play, SEM Seminar, DM-Demo/Lab, WS- Web Search, PJ Project & CG -GAP. IX. Lesson Schedule: No. Unit No / Description Duration (Date) Total From To No of Periods No of Inst. Objectives Achieved 1. Unit 1 9 IO1 2. Unit 2 9 IO2 3. Unit 3 9 IO3 4. Unit 4 9 IO4 5. Unit 5 9 IO5&IO6 Remarks

6 IX. Unit Test / CAT Test: No. Date UNIT / CAT Portion No. Date UNIT / CAT Portion 1 UNIT-I 4 UNIT-IV 2 UNIT-II 5 UNIT-V 3 UNIT-III XI.Internal / Model Test: No. Tentative Date Portion Total Appear Pass % ` XII.Assignments: Unit Suggestion* Group / Topic No. Individual 1 Building a data ware house T1 IA 3 Data mining Functionalities T1 IA Partition and Hierarchical T1 IA 5 methods * Suggestion: Books / Journals / Magazines / Web Resources Objectives / Outcome Announced Date Submitted Date XIII.Tutorial: Unit No. Topic * (Questions /Problem /Exercises) 1. Questions & Problems 2. Questions & Problems 3. Questions & Problems 4. Questions & Problems 5. Questions & Problems Objectives / Outcome Question Count *Attach Proof of Tutorial Sheets Separate in given Format. Discussed Date Completed Date Staff In Charge Head of the Department Principal

SCHEME OF COURSE WORK. Data Warehousing and Data mining

SCHEME OF COURSE WORK. Data Warehousing and Data mining SCHEME OF COURSE WORK Course Details: Course Title Course Code Program: Specialization: Semester Prerequisites Department of Information Technology Data Warehousing and Data mining : 15CT1132 : B.TECH