Name of The Course Course Code Version No Prerequisite Co requisite Anti- requisite L Data Mining and Data Warehousing 3 BSCS3530 Date of Approval: 8/6/2021 Database Management System T P C 0 0 3 Course Objectives: 1. To interpret the contribution of data warehousing and data mining to the decision support level of organizations 2. To evaluate different models used for OLAP and data pre-processing 3. To categorize and carefully differentiate between situations for applying different data mining techniques: mining frequent pattern, association, correlation, classification, prediction, and cluster analysis Course Outcomes At the end of the course student will be able to: CO1 Understand the functionality of the various data mining and data warehousing component CO2 Explain the analyzing techniques of various data. CO3 Develop skill in selecting the appropriate data mining algorithm . CO4 Master data mining techniques in various applications like social, scientific and environmental context. CO5 Compare different approaches of data ware housing and data mining with various technologies. CO6 Gain knowledge of latest trends and research areas in the course. Text Book (s) 1. Data Mining- Concepts & Techniques; Jiawei Han &MichelineKamber- 2001, Morgan Kaufmann. 2. Data Warehousing In the Real World; Sam Anahory& Dennis Murray; 1997, Pearson Reference Book (s) 1. Data Warehousing, Data Miniing and OLTP; Alex Berson, 1997, McGraw Hill. 2. Building the Data Warehouse; W.H. Inman, 1996, John Wiley & Sons. 3. Developing the Data Warehouses; W.H Ionhman,C.Klelly, John Wiley & Son Unit-1 Introduction to Data Mining 8 hours Introduction – Data – Types of Data – Data Mining Functionalities – Interestingness of Patterns – Classification of Data Mining Systems – Data Mining Task Primitives – Integration of a Data Mining System with a Data Warehouse – Issues –Data Preprocessing. Unit-2 Association Rule Mining 8 hours Association Rule - Mining Frequent Patterns, Associations and Correlations – Mining Methods – Mining various Kinds of Association Rules – Correlation Analysis – Constraint Based Association Mining Unit-3 Classification 8 hours Classification and Prediction – Basic Concepts – Decision Tree Induction – Bayesian Classification – Rule Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification Methods – Prediction. Unit-4 Clustering 8 hours Cluster Analysis – Types of Data – Categorization of Major Clustering Methods – Kmeans– Partitioning Methods – Hierarchical Methods – Density-Based Methods –Grid Based Methods – Model-Based Clustering Methods – Clustering High Dimensional Data – Constraint – Based Cluster Analysis – Outlier Analysis – Data Mining Applications. Unit-5 Introduction to Data Warehouse 8 hours Introduction to Data Ware House, Differences between operational data base systems and data Ware House, Data Ware House characteristics, Data Ware House Architecture and its components, Extraction-Transformation-Loading, Logical (Multi Dimensional), Data Modeling, Schema Design, star and snow-Flake Schema, Fact Constellation, Fact Table Unit-6 4 hours Advancement & Research Advancement in the course: Real Time Data warehousing, Steam Mining , Research methodologies, research discussion & publication Continuous Assessment Pattern Internal (IA) 20 Assessment Mid Term (MTE) 30 Test End Term (ETE) 50 Test Total Marks 100