Data warehousing and Data Mining (171601)

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Data warehousing and Data Mining

PPT of Chapter I

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Syllabus
Teaching Hours: 3
Practical Hours 2
Credits 5

Introduction to Data Warehousing
Why reporting and Analyzing data, Raw data to valuable information-Lifecycle of Data - 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.

Introduction to Data Mining
Motivation for Data Mining - Data Mining: On What kind of Data? -Definition and Functionalities: What kind of patterns can be mined? - Classification of DM Systems – Integration of a Data Mining system with a Database or a Data Warehouse - Issues in DM – KDD Process

Data Preprocessing and Data Mining Primitives
Why Preprocess the Data? – Data Cleaning – Data Integration and Transformation – Data Reduction – Discretization and Concept Hierarchy Generation – Data Mining Primitives: What Defines a Data Mining Task?

Concept Description and Association Rule Mining
What is concept description? - Data Generalization and summarization-based characterization - Attribute relevance - class comparisons 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

Classification and Clustering
What is classification and prediction? – Issues regarding Classification and prediction: Classification methods: Decision tree, Bayesian Classification, Rule based, CART, Neural Network, CBR, Rough set Approach, Fuzzy Logic, Genetic Algorithms – Prediction methods: Linear and non linear regression, Logistic Regression – What is Cluster Analysis? – Types of Data in Cluster Analysis – A Categorization of Major Clustering Methods, Types of Clustering Algorithms

Advance Topics of Data Mining and its Applications
Mining Time-Series and Sequence Data – Mining Text Databases – Mining the World Wide Web – Data Mining Application