DOC/LP/01/28.02.02


                                LESSON PLAN                                    LP - CS1004
          ...
DOC/LP/01/28.02.02
                                LESSON PLAN                                   LP - CS1004
             ...
DOC/LP/01/28.02.02
                                 LESSON PLAN                                   LP - CS1004
            ...
DOC/LP/01/28.02.02
                                LESSON PLAN                                     LP - CS1004
           ...
LESSON PLAN                                LP - CS1004
                                                                   ...
DOC/LP/01/28.02.02
                                   LESSON PLAN                                           LP - CS1004
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CS1004

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CS1004

  1. 1. DOC/LP/01/28.02.02 LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: I Branch: CSE Semester: 6 Page 1 of 6 Unit I : INTRODUCTION AND DATAWAREHOUSING Introduction, Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Implementation, Further Development, Data Warehousing to Data Mining Objective: Here the students learn the basics of Data mining and Data Warehousing.The difference between Database and Data Warehouse are discussed in Detail. Implementations of Data Warehouse using DMQL are made known to students. Session Topics to be covered Time Books Teaching No Allocation Referred Method (min) 1 Motivation towards Data mining. Data mining 50 1 BB - Definition, Process of KDD. 2 Architecture of Data mining systems. 50 1 BB Data mining on different databases. 3 Introduction to Data mining Functionalities. 50 1 BB 4 Concept/class Description: characterization 50 1 BB and Discrimination, Association analysis, Cluster analysis, Classification and Prediction And outlier analysis. 5 Classification of Data Mining systems. 50 1 BB OLAP and OLTP,Cuboids, star/snowflake and Fact constellation schema 6 Introducing Concept Hierarchies, OLAP 50 1,5 BB operations on Multidimensional Data Models 7 A 3-tier Data warehouse architecture, Types of 50 1,5 BB OLAP servers. 8 Data warehouse Implementation: Compute 25 1,5 BB cube operator, Partial Materialization, Multiway array aggregation of Data cube 25 computation. 9 Metadata Repository, Integrated OLAM and 50 1,5 BB OLAP architecture. 10 Issues in OLAP indexing. 50 1,5 BB
  2. 2. DOC/LP/01/28.02.02 LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: II Branch: CSE Semester: 6 Page 2 of 6 Unit II: DATA PREPROCESSING, LANGUAGE, ARCHITECTURES, CONCEPT DESCRIPTION Why Preprocessing, Cleaning, Integration, Transformation, Reduction, Discretization, Concept Hierarchy Generation, Data Mining Primitives, Query Language, Graphical User Interfaces, Architectures, Concept Description, Data Generalization, Characterizations, Class Comparisons, Descriptive Statistical Measures. Objective: To study and analyze the preprocessing, cleaning and Integration techniques. Here the students get first hand exposure to DMQL and its implementation issues.students also learns the functionalities of data mining. Session Topics to be covered Time Books Teaching No Allocation Referred Method (min) 11 Data Cleaning and Noisy data. 50 1 BB 12 Data Integration and Transformation. 50 1 BB 13 Data Reduction, aggregation and dimension 20 1 BB Reduction, compression techniques. 30 14 PCA, Numerosity reduction. 50 1 BB 15 Discretization and Concept Hierarchy 50 1 BB generation. 16 Defining a DM task. DMQL –syntax and 50 1 BB examples for major functionalities. 17 Architectures of DM systems. 35 1 BB Concept Description, generalization and 15 summarization. 18 Attribute –Oriented Induction. Presentation of 50 1 BB Derived generalizations. Attribute Relevance analysis. 19 Descriptive statistical measures. 15 1 BB Measuring central tendency, dispersion of data 35 20 Graphical displays of DSM. 50 1 BB 21 Problems on quartiles,boxplots,outliers 50 1 BB 22 CAT – I 60
  3. 3. DOC/LP/01/28.02.02 LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: III Branch: CSE Semester: 6 Page 3 of 6 Unit III :ASSOCIATION RULES Association Rule Mining, Single-Dimensional Boolean Association Rules from Transactional Databases, Multi-Level Association Rules from Transaction Databases Objective: The students learn association mining and algorithms that perform single& multi Level dimensional rule mining. Session Topics to be covered Time Books Teaching No Allocation Referred Method (Min) 23 Association Rule mining – an introduction. 50 1 BB 24 Mining single dimensional Boolean 50 1 BB association rules 25 The Apriori Algorithm: Finding frequent item 35 1 BB Sets using Candidate generation 15 26 Mining frequent item sets without candidate 50 1 BB Generation, frequent pattern growth algorithm 27 Iceberg queries, mining multilevel association 50 1 BB rules from Transactional databases. 28 Approaches to Mining multilevel association 50 1 BB rules. 29 Mining multi dimension association rules from 25 1 BB Relational databases 25 30 Mining multi dimension association rules 50 1 BB using static discretization of quantitative attributes. 31 Mining distance based association rules. 50 1 BB 32 Constraint based association mining 50 1 BB 33 Meta rule – guided Mining of association rules 50 1 BB
  4. 4. DOC/LP/01/28.02.02 LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: IV Branch: CSE Semester: 6 Page 4 of 6 Unit IV- CLASSIFICATION AND CLUSTERING Classification and Prediction, Issues, Decision Tree Induction, Bayesian Classification, Association Rule Based, Other Classification Methods, Prediction, Classifier Accuracy, Cluster Analysis, Types of data, Categorization of methods, Partitioning methods, Outlier Analysis. Objective: To study various classification methods like Bayesian, DTI and cluster analysis. Here Outlier analyses are studied in detail. Session Topics to be covered Time Books Teaching No Allocation Referred Method (Min) 34 Classification and Prediction 50 1 BB 35 Classification by decision tree induction 50 1 method. BB 36 Tree pruning. Extracting classification rules 50 1 BB from decision trees. 37 Bayesian classification, bayes theorem, 50 1 BB Bayesian belief networks. 38 A multilayer Feed-forward neural Network. 50 1 BB Association rule based classification. 39 Classifier Accuracy and Increasing accuracy. 50 1 BB 40 Cluster Analysis, types of data. 50 1 BB 41 Partitioning methods – K Means and K 50 1 BB -medoids 42 Statistical based outlier detection 50 1 BB distance based outlier detection 43 Deviation based outlier detection 50 1 BB 44 CAT -II 60 DOC/LP/01/28.02.02
  5. 5. LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: V Branch: CSE Semester: 6 Page 5 of 6 Unit V-RECENT TRENDS Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Spatial Databases, Multimedia Databases, Time Series and Sequence Data, Text Databases, World Wide Web, Applications and Trends in Data Mining Objective: Here the student gets exposure over Text Databases, Web Databases, Spatial Databases and Multimedia Databases. Thorough understanding of this chapter would. Help the student to carry out research work in this area. Topics to be covered Time Books Teaching Session Allocation Referred Method No (min) 45 Multidimensional Analysis and Descriptive 50 1 BB Mining of Complex Data Objects 46 Aggregation and approximation in spatial and 50 1 BB Multimedia Data generalization. 47 Mining spatial Databases ,Spatial OLAP 20 1 BB Spatial assoc and cluster analysis. 30 48 Mining Multimedia Databases 50 1 BB 49 Mining Time series and sequence data 10 1 BB Similarity search in time –series Analysis 40 50 Mining Text Databases 25 1 BB Text Data analysis and Information retrieval 25 51 Mining WWW 15 1 BB Identification of Authoritative web pages. 35 52 Web Usage mining 50 1 BB 53 CAT -III 60
  6. 6. DOC/LP/01/28.02.02 LESSON PLAN LP - CS1004 LP Rev. No:00 Sub Code & Name: CS 1004 – DATA WAREHOUSING AND MINING Date: 7.12.09 Unit: V Branch: CSE Semester: 6 Page 6 of 6 Course Delivery Plan: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 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 I II I II I II Units TEXT BOOK 1.J. Han, M. Kamber, “ Data Mining: Concepts and Techniques ” , Harcourt India Morgan Kauffman, 2001. REFERENCES 2.Margaret H.Dunham, “ Data Mining: Introductory and Advanced Topics ” , Pearson Education 2004. 3.Sam Anahory, Dennis Murry, “ Data Warehousing in the real world ” , Pearson Education 2003. 4.David Hand, Heikki Manila, Padhraic Symth, “ Principles of Data Mining ” , PHI 2004. 5.W.H.Inmon, “ Building the Data Warehouse ” , 3 rd Edition, Wiley, 2003. Alex Bezon, Stephen J.Smith, “ Data Warehousing, Data Mining & OLAP ” , MeGraw- Hill Edition, 2001. Prepared by Approved by Signature Name Prof. R.NEDUNCHELIAN Dr. SUSAN ELIAS Ms. S.PUSHPA Designation Prof/CSE HOD – CSE Asst. Prof/CSE Date

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