1. D.A. Patterson and J.L.Hennessey, "Computer organization and Design", Morgan Kaufmann, 2nd Edition. V.Rajaram & C.S.R.Murthy, "Parallel Computer", PHI. Wilkinson and Michael Allen, Programming” Pearson Education. “Parallel CS/IT 423 DATA ENGINEERING Lectures : 4 periods/week, Tutorial: 1 Sessional Marks : 30 University Exam : 3 University Examination Marks : 70 UNIT I (18 Periods) – Data Warehouse Introduction, A Multi-dimensional data model, Data Warehouse – Architecture, Data Warehouse Implementation. Data Mining Introduction, Data Mining, Kinds of Data, Data Mining Functionalities, – Classification of Data Mining Systems, Major issues in Data Mining. UNIT II (18 Periods) – Data Preprocessing Data cleaning, Data Integration & Transformation, Data Reduction, – Discretization & Concept Hierarchy Generation, Data Mining Primitives. Mining Association roles in large databases Association rule mining, mining – Boolean Association rules from Transactional Databases, Mining Multi-dimensional Association rules from relational databases & Data Warehouses. UNIT III (15 Periods) – Cluster Analysis Introduction, of data in Cluster analysis, A categorization of major – clustering methods, partitioning methods, Hierarchical methods, Density-Based Methods: DBSCAN, Grid-based Method: STING; Model-based Clustering Method: Statistical approach, Outlier analysis. UNIT IV (20 Periods) – Classification & Prediction Introduction, Classification by Decision tree induction, Bayesian – Classification, , Classification by Back propagation, Other Classification Methods, Prediction, Classifier accuracy. Mining Complex Type of Data Multidimensional Analysis and Descriptive Mining of Complex – Data Objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Text Databases, Mining the World Wide Web. Textbooks: Data Mining Concepts & Techniques Jiawei Han Micheline Kamber Morgan Kaufmann – – Publishers. Reference Books: Data Warehouse Toolkit Ralph Kinball John Wiley Publishers. – – Data Mining (Introductory and Advanced Topics) Margaret H.Dunham Pearson – – Education. Data Warehousing in the real world A Practical guide for Building decision support – systems Sam Anahory, Dennis Murray Pearson Education. – – Introduction to Data Mining with case studies G.K.Gupta, PHI Publications, – 2006 CS/IT 424 INDUSTRIAL ENGINEERING & MANAGEMENT
1. D.A. Patterson and J.L.Hennessey, "Computer organization and Design", Morgan Kaufmann, 2nd Edition. V.Rajaram & C.S.R.Murthy, "Parallel Computer", PHI. Wilkinson and Michael Allen, “Parallel Programming” Pearson Education. CS/IT 423 DATA ENGINEERING Lectures : 4 periods/week, Tutorial: 1 Sessional Marks : 30 University Exam : 3 University Examination Marks : 70 UNIT – I (18 Periods) Data Warehouse – Introduction, A Multi-dimensional data model, Data Warehouse Architecture, Data Warehouse Implementation. Data Mining – Introduction, Data Mining, Kinds of Data, Data Mining Functionalities, Classification of Data Mining Systems, Major issues in Data Mining. UNIT – II (18 Periods) Data Preprocessing – Data cleaning, Data Integration & Transformation, Data Reduction, Discretization & Concept Hierarchy Generation, Data Mining Primitives. Mining Association roles in large databases – Association rule mining, mining Boolean Association rules from Transactional Databases, Mining Multi-dimensional Association rules from relational databases & Data Warehouses. UNIT – III (15 Periods) Cluster Analysis – Introduction, of data in Cluster analysis, A categorization of major clustering methods, partitioning methods, Hierarchical methods, Density-Based Methods: DBSCAN, Grid-based Method: STING; Model-based Clustering Method: Statistical approach, Outlier analysis. UNIT – IV (20 Periods) Classification & Prediction – Introduction, Classification by Decision tree induction, Bayesian Classification, , Classification by Back propagation, Other Classification Methods, Prediction, Classifier accuracy. Mining Complex Type of Data – Multidimensional Analysis and Descriptive Mining of Complex Data Objects, Mining Spatial Databases, Mining Multimedia Databases, Mining Text Databases, Mining the World Wide Web. Textbooks: Data Mining Concepts & Techniques – Jiawei Han Micheline Kamber – Morgan Kaufmann Publishers. Reference Books: Data Warehouse Toolkit – Ralph Kinball – John Wiley Publishers. Data Mining (Introductory and Advanced Topics) – Margaret H.Dunham – Pearson Education. Data Warehousing in the real world – A Practical guide for Building decision support systems – Sam Anahory, Dennis Murray – Pearson Education. Introduction to Data Mining with case studies – G.K.Gupta, PHI Publications, 2006 CS/IT 424 INDUSTRIAL ENGINEERING & MANAGEMENT
anu_b.tech_cse_Syllabus_2007_08.docx