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Business Data Warehousing & Data Mining (IT05)

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This book is designed to meet the needs of master of business administration students studying for the very first time in their curriculum. Thus complexity of the matter has been avoided with a view that complete course content has to be completed by the student in limited time period. The subject matter has been presented in a lucid, comprehensive and systematic manner which is easy to understand and also develops writing ability for students to score good marks in upcoming examination. This book includes all types of questions according to new pattern of university. Course content has been divided into topic wise and in chapter wise form according to curriculum framed by Dr. A.P.J. Abdul Kalam Technical University, Lucknow. This book includes unsolved papers of last years and sample paper. We hope that this book will be successful in its objectives and will receive appreciation from students and teachers alike.

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SKU: KMBN-IT05 Categories: , ,
ISBN 978-93-88783-16-2
Publishers R. Narain & Co.
Author Aditya Kumar
Binding Paperback
Pages  112
Subject Business Data Warehousing & Data Mining

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Weight 200 g

Table of Content

Unit 1: Data Warehousing: Overview, Definition, Data Warehousing Components, Difference between Database System and Data Warehouse, Characteristics, Functionality and Advantages; Metadata: Concepts and classifications; Multi-Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, 3 Tier Architecture, ETL, Data Marting ,Concept Hierarchy, Use of Data warehousing in Current Industry Scenario, Case Study.
Unit 2: Data Visualization and Overall Perspective: Aggregation, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining.
Unit 3: Data Mining: Overview, Motivation, Definition & Functionalities, difference between data mining and Data Processing, KDD process, Form of Data Preprocessing, Data Cleaning. : Missing Values, Noisy Data, Binning, Clustering, Regression, Computer and Human inspection, Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression. Applications of Data Mining in today’s world.
Unit 4: Data Mining Techniques: Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Association rules: Introduction, Large Item sets, Basic Algorithms, Apriori Analysis, Generating Filtering Rules, Target Marketing, Risk Management, Customer profiling,.
Unit 5: Classification: Definition Decision Tree-Based Algorithms, Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitioned Algorithms. Hierarchical Clustering- CURE and Chameleon. Parallel and Distributed Algorithms, Neural Network approach, Business , Data mining Case study, Applications of Data Mining, Introduction of data mining tools like WEKA, ORANGE , SAS, KNIME etc.

Features

Salient features include:

  1. Comprehensive coverage of theoretical aspects in management concept & Indian ethos.
  2. All intricate aspects are explained by simple, lucid and specific explanations and substantiated with neat and elaborate diagrammatic sketches.

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