Business Intelligence and Data Mining

Overview and concepts Data Warehousing and Business Intelligence

Why reporting and Analyzing 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, Virtual Warehouses – Overview of the components – Metadata in the data warehouse – Need for data warehousing – Basic elements of data warehousing, Architectures, OLAP and OLAP Servers – recent trends in data warehousing, Dynamic Warehousing.

The Architecture of BI

BI and DW architectures and its types – Relation between BI and Data Mining.

Introduction to data mining (DM)

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- Various Models and their significance.

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 algorithms, FP Growth Algorithm – Incremental ARM – Associative Classification – Rule Mining, ARCS.

Classification and Prediction

What is classification and prediction? – Issues regarding Classification and prediction:
• Various Classifiers and Classification methods: Decision tree, Bayesian Classification, Rule Based Classifiers, CART, Neural Network, Nearest Neighbour, Case Based Reasoning, Rough Set Approach. The role of Genetic Algorithm and fuzzy logic.
• Prediction methods: Linear and non linear regression, Logistic Regression.

Data Mining for Business Intelligence Applications