<p> DOC/LP/01/28.02.02</p><p>LESSON PLAN LP – CS 9264 Revision No: 00</p><p>Sub code & Name : CS9264 Date: 28.01.10 Data Warehousing and Data Mining Page 1 of 6 Unit : I Branch: M.E(CS) Semester: II</p><p>UNIT I: 9</p><p>Data Warehousing and Business Analysis: - Data warehousing Components –Building a Data warehouse – Mapping the Data Warehouse to a Multiprocessor Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools – Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis. </p><p>Objective: To know the relationship between Data Warehousing and Business,Data warehouse Architecture Metadata – reporting – Query tools and Applications – Online Analytical Processing (OLAP) – OLAP and Multidimensional Data Analysis. </p><p>Session Topics to be covered Time REF Teaching No Allocation method 1 Introduction Data warehousing and Business 50m 1 BB 2 Data warehousing Components –Building a Data 50m 1 BB warehouse 3 Mapping the Data Warehouse to a Multiprocessor 50m 1,3 BB Architecture 4 DBMS Schemas for Decision Support 50m 1 BB 5 Data Extraction, Cleanup, and Transformation 50m 1,2 BB Tools 6 Metadata – reporting 50m 1 BB 7 Query tools and Applications 50m 1,2,3 BB 8 Online Analytical Processing (OLAP) 50m 1 BB 9 OLAP and Multidimensional Data Analysis. 50m 1 BB DOC/LP/01/28.02.02</p><p>LESSON PLAN LP – CS 9264 Revision No: 00</p><p>Sub code & Name : CS9264 Date: 28.01.10 Data Warehousing and Data Mining Page 2 of 6 Unit : II Branch: M.E(CS) Semester: II</p><p>UnitII 9</p><p>Data Mining: - Data Mining Functionalities – Data Preprocessing – Data Cleaning – Data Integration and Transformation – Data Reduction – Data Discretization and Concept Hierarchy Generation. Association Rule Mining: - Efficient and Scalable Frequent Item set Mining Methods – Mining Various Kinds of Association Rules – Association Mining to Correlation Analysis – Constraint-Based Association Mining.</p><p>Objective:</p><p>To make the students know about the concepts of improving efficiency in mining process by using various data preprocessing techniques and also the use of association rule mining in huge databases.</p><p>Session Topics to be covered Time REF Teaching No Allocation method 10 Data Mining Functionalities 50m 1,4 BB 11 Data Preprocessing-Data Cleaning & integration 50m 1,4 BB 12 Transformation ,Reduction – aggregation 50m 1,4 BB 13 Dimension reduction and compression 50m 1,4 BB 14 Discretization Concept Hierarchies 50m 1 BB 15 Association Rule Mining: - Efficient and Scalable 50m 1 BB Frequent Item set Mining Methods 16 Mining Various Kinds of Association Rules 50m 1 BB 17 Association Mining to Correlation Analysis 50m 1 BB 18 – Constraint-Based Association Mining. 50m 1 BB</p><p>19,20 CAT I 90m DOC/LP/01/28.02.02</p><p>LESSON PLAN LP – CS 9264 Revision No: 00</p><p>Sub code & Name : CS9264 Date: 28.01.10 Data Warehousing and Data Mining Page 3 of 6 Unit : III Branch: M.E(CS) Semester: II</p><p>UNIT III 9 Classification and Prediction: - Issues Regarding Classification and Prediction – Classification by Decision Tree Introduction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction – Accuracy and Error Measures – Evaluating the Accuracy of a Classifier or Predictor – Ensemble Methods – Model Section. Objective:</p><p>To make the students know about the forms of data analysis that can be used to extract models describing important data classes and also to predict future trends.</p><p>Session Topics to be covered Time REF Teaching No Allocation method 21 Issues Regarding Classification And Prediction 50m 1 BB 22 Decision tree Induction – classification 50m 1 BB 23 Bayesian classification 50m 1 BB 24 Rule Based Classification 25 Back propagation method 50m 1 BB 26 Support Vector Machines 50m 1 BB 27 Associative Classification – Lazy Learners 50m 1 BB 28 Prediction – Accuracy and Error Measures 50m 1 BB 29 Evaluating the Accuracy of a Classifier or Predictor 50m 1 BB 30 Ensemble Methods – Model Section. 50m 1 BB 50m 1</p><p>DOC/LP/01/28.02.02</p><p>LESSON PLAN LP – CS 9264 Revision No: 00</p><p>Sub code & Name : CS9264 Date: 28.01.10 Data Warehousing and Data Mining Page 4 of 6 Unit : IV Branch: M.E(CS) Semester: II</p><p>UNIT IV 9 Cluster Analysis: - Types of Data in Cluster Analysis – A Categorization of Major Cluster- ing Methods – Partitioning Methods – Hierarchical methods – Density-Based Methods – Grid-Based Methods – Model-Based Clustering Methods – Clustering High-Dimensional Data – Constraint-Based Cluster Analysis – Outlier Analysis Objective:</p><p>To study about the Cluster analysis and its application in Data mining with various Clustering methods. Session Topics to be covered Time REF Teaching No Allocation method 31 Cluster Analysis: - Types of Data in Cluster 50m 1 BB Analysis 32 A Categorization of Major Clustering Methods – 50m 1,5 BB Partitioning Methods – Hierarchical methods 33 A Categorization of Major Clustering Methods – 50m 1,4,5 BB Partitioning Methods – Hierarchical methods 34 Density-Based Methods 50m 1,4,5 BB 35 – Grid-Based Methods 50m 1 BB 36 Model-Based Clustering Methods 50m 1 BB 37 Clustering High-Dimensional Data 50m 1,2 BB 38 Constraint-Based Cluster Analysis – Outlier Analy- 50m 1,2 BB sis 39,40 CAT II 90m DOC/LP/01/28.02.02</p><p>LESSON PLAN LP – CS 9264 Revision No: 00</p><p>Sub code & Name : CS9264 Date: 28.01.10 Data Warehousing and Data Mining Page 5 of 6 Unit : V Branch: M.E(CS) Semester: II</p><p>UNIT V 9 Mining Object, Spatial, Multimedia, Text and Web Data: Multidimensional Analysis and Descriptive Mining of Complex Data Objects – Spatial Data Mining – Multimedia Data Mining – Text Mining – Mining the World Wide Web.</p><p>Objective:</p><p>To examine few application domains and to discuss how customized data mining tools should be developed for such applications.</p><p>Session Topics to be covered Time REF Teaching No Allocation method 41 Data mining applications 50m 1 BB 42 Mining Object, Spatial, Multimedia, Text and Web 50m 1 BB Data: 43 Multidimensional Analysis and Descriptive Mining 50m 1 BB of Complex Data Objects 44 Spatial Data Mining 50m 1 BB 45 Multimedia Data Mining 50m 1 BB 46 Text Mining 50m 1 BB 47 Mining the World Wide Web 50m 1 BB 48 CAT III Course delivery plan </p><p>1 2 3 4 5 6 7 8 9 10 11 12 Week I II I II I II I II I II I II I II I II I II I II I II I II</p><p>Units</p><p>REFERENCES</p><p>1. Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Second Edition, 2. Elsevier, Reprinted 2008. 3. Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Tenth Reprint 2007. 4. K.P. Soman, Shyam Diwakar and V. Ajay “Insight into Data mining Theory and Practice”, Easter Economy Edition, Prentice Hall of India, 2006. 5. G. K. Gupta “Introduction to Data Mining with Case Studies”, Easter Economy Edition, Prentice Hall of India, 2006. 6. Pang-Ning Tan, Michael Steinbach and Vipin Kumar “Introduction to Data Mining”, Pearson Education, 2007.</p><p>Prepared by Approved by Signature Name R.Nedunchelian Dr Susan Elias</p><p>Designation Professor HOD / CS</p><p>Date 28.01.2010 28.01.2010</p>