RAJALAKSHMI ENGINEERING COLLEGE

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RAJALAKSHMI ENGINEERING COLLEGE

  1. 1. RAJALAKSHMI ENGINEERING COLLEGE Thandalam, Chennai – 602 105 LESSON PLAN Faculty Name : L.Priya Code : Subject Name : Data Warehousing & Mining Code : CS1004 Year : IV Semester : VI Degree & Branch : B.Tech - IT Section : B Aim: To serve as an introductory course to under graduate students with an emphasis on the design aspects of Data Mining and Data Warehousing. Objectives: This course has been designed with the following objectives: • To introduce the concept of data mining with in detail coverage of basic tasks, metrics, issues, and implication. Core topics like classification, clustering and association rules are exhaustively dealt with. • To introduce the concept of DW & its architecture and design. Text Book: 1. J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Harcourt India / Morgan Kauffman, 2001 Reference Book: 1. Margaret H.Dunham, “Data Mining: Introductory and Advanced Topics”, Pearson Education 2004. 2. Sam Anahory, Dennis Murry, “Data Warehousing in the real world”, Pearson Education 2003. 3. David Hand, Heikki Manila, Padhraic Symth, “Principles of Data Mining”, PHI 2004. 4. W.H.Inmon, “Building the Data Warehouse”, 3rd Edition, Wiley, 2003. 5. Alex Bezon, Stephen J.Smith, “Data Warehousing, Data Mining & OLAP”, MeGraw-Hill Edition, 2001. 6. Paulraj Ponniah, “Data Warehousing Fundamentals”, Wiley-Interscience Publication, 2003 Signature of Faculty Signature of HOD 1
  2. 2. Topic T / R* Sl.No. Date Period Unit Page(s) Book Introduction, Data Mining- 1 I T1 1-20 Kinds of data Data Mining 21-26 Functionalities, 28-30 2 T1 Classification of DM Systems 3 Performance Issues in DM T1 30-33 Datawarehouse, 39-51 4 Multidimensional Data T1 Model 5 Examples of Schemas T1 52-56 Concept Hierarchies, OLAP 56-61 6 T1 Operations Datawarehouse 62-81 Architecture, 7 T1 Datawarehouse Implementation Metadata, Development of 83-92 8 T1 Data Cube Technology 9 DW To DM T1 93-95 Data Preprocessing , Data 105-112 10 II T1 Cleaning Data Integration & 112-114 11 T1 Transformation Data Reduction, 116-138 12 T1 Discretization 13 DM Primitives T1 146-157 14 DM Query Language T1 159-169 GUI & Architectures of DM 170-173 15 T1 Systems Data Generalization, 181-198 16 T1 Analytical Characterization 17 Mining Class Comparisons T1 200-207 Descriptive Statistical 208-217 18 T1 Measures 19 III Association Rule Mining T1 226-228 20 Association Rule Mining T1 229-230 1-D Boolean Association 230-236 21 T1 Rules from TD 1-D Boolean Association 236-244 22 T1 Rules from TD Multilevel Association Rule 244-246 23 T1 from TD Multilevel Association Rule 244-246 24 T1 from TD Signature of Faculty Signature of HOD 2
  3. 3. Topic T / R* Sl.No. Date Period Unit Page(s) Book Approaches to Mining 246-250 25 Multilevel Association T1 Rules Approaches to Mining 246-250 26 Multilevel Association T1 Rules Classification & Prediction 279-283 27 IV T1 Issues 28 Decision Tree Induction T1 284-295 29 Bayesian Classification T1 296-301 30 Association Rule Mining T1 311-314 Other Classification 314-319 31 T1 Methods 32 Prediction T1 319-322 33 Classifier Accuracy T1 322-326 34 Cluster Analysis T1 335-337 35 Types of Data T1 338-346 36 Categorization of Methods T1 346-348 Partitioning Methods, 348-354 37 T1 Outlier Analysis 381-388 Multidimensional Analysis 396-404 38 V & Descriptive Mining of T1 Data Objects 39 Mining Spatial Databases T1 405-412 Mining Multimedia 412-418 40 T1 Databases Mining Time-series & 418-427 41 T1 Sequence Data 42 Mining Text DBs & WWW T1 428-442 43 DM Applications T1 451-457 DM System Products & 457-462 44 T1 Research Additional Themes on DM, 462-478 45 Social Impacts & Trend- T1 DM Signature of Faculty Signature of HOD 3

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