Unit 1 Overview and concepts Data Warehousing and Business

• Intelligence Why reporting and Analysing data
• Raw data to valuable information- Lifecycle of Data - What is Business Intelligence - BI and DW in today’s perspective - What is data warehousing - The building Blocks: Defining Features - Data warehouses and data marts - Overview of the components - Metadata in the data warehouse - Need for data warehousing - Basic elements of data warehousing - trends in data warehousing 8 13.33

Unit 2 Introduction to data mining (DM)

6 lecture hours

• Motivation for Data Mining - Data Mining-Definition and Functionalities – Classification of DM Systems - DM task primitives - Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process

Unit 3 Data Pre-processing

• Why to pre-process data? - Data cleaning: Missing Values
• Noisy Data - Data Integration and transformation - Data Reduction: Data cube aggregation
• Dimensionality reduction - Data Compression - Numerosity Reduction - Data Mining Primitives - Languages and System Architectures: Task relevant data - Kind of Knowledge to be mined - Discretization and Concept Hierarchy 8 13.33

Unit 4 Concept Description and Association Rule Mining

• What is concept description? - Data Generalization and summarization- based characterization - Attribute relevance - class comparisons 8 13.33 Association Rule Mining: Market basket analysis - basic concepts - Finding frequent item sets: Apriori algorithm - generating rules – Improved Apriori algorithm – Incremental ARM – Associative Classification – Rule Mining

Unit 5 Classification and Prediction

• What is classification and prediction? – Issues regarding Classification and prediction: • Classification methods: Decision tree
• Bayesian Classification
• Rule based
• Neural Network • Prediction methods: Linear and nonlinear regression
• Logistic Regression - Introduction of tools such as DB Miner /WEKA/DTREG DM Tools 10 16.67

Unit 6 Data Mining for Business Intelligence Applications

6 lecture hours

• Data mining for business Applications like Balanced Scorecard
• Fraud Detection
• Clickstream Mining
• Market Segmentation
• retail industry
• telecommunications industry
• banking & finance and CRM etc

Unit 7 Advance topics

• Introduction and basic concepts of following topics
• Multirelational Data Mining
• Spatial mining
• web mining
• text mining
• Ensemble Classifier (Multiple Classifier
• Incremental learning 4 6.67