Business Intelligence and Data Mining for GTU ( VIII-Comp-2008 course )

By Manmohan Singh
Publisher: Technical Publications
ISBN: 9789333201841
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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 warehouse 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 - Defination 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 summariazation-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
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