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Data Mining and Warehousing

By Charu Chhabra

5 Ratings | 1 Reviews

Rs. 153

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Specifications of Data Mining and Warehousing

Book Details

  • 978-93-82247-10-4
  • English
  • 2012, 2013, 2014
  • Paper Back
  • 284

Contents

  • UNIT–1
    1. Overview to Data Mining
    Objectives, Introduction, Motivation for Data Mining, Data Mining on different Kind of Data or Data Mining on What Kind of Data, Data Mining Functionalities or Functionalities of Data Mining, Data Mining Patterns, Summary, Review Questions.
     
    2. Data Preprocessing
    Objectives, Introduction, Data Preprocessing, Forms of Data Pre-processing, Data Cleaning, Computer Human Inspections, Inconsistent Data, Data Integration and Transformations, Data Integration, Data Transformations, Data Reduction, Data Cube Aggregation, Attributed Subset Selection, Data Compression, Numerosity Reduction, Discretization and Concept Hierarchy Generation, Summary, Review Questions.
     
    UNIT–2
    3. Concept Description
    Objectives, Introduction, Data Generalization, Data Cube Approach (or OLAP approach), Attribute Oriented Induction (AOI), How Attribute Oriented Industries is Performed, Implementation of Attribute Oriented Induction, Data Cube Implementation of Attribute-Oriented Induction, Analytical Characterization, Why Perform Attribute Relevance Analysis?, Methods of Attribute Relevance Analysis, Mining Class Comparisons: Discrimination between Different Classes, Class Comparison Methods and Implementations, Presentation of Class Comparison Descriptions, Mining Descriptive Statistical Measure in Large Databases, Measuring the Central Tendency, Measuring the Dispersion of Data, Histogram, Quantile Plot, Q-Q Plot or Quantile-Quantile Plot, Scatter Plot, Loess Curves, Summary, Review Questions.
     
    4. Mining Association Rule in Large Databases
    Objectives, Introduction, Association Rule Mining, Market Basket Analysis, Basic Concepts, Association Rule Mining, Mining Single-Dimensional Boolean Association Rules from Transactional Databases, The Apriori Algorithm-Finding frequent Itemsets, Apriori Algorithm an Example, Mining Multilevel Association Rules from Transactional Databases, Multilevel Association Rules, Approaches to Mining Multilevel Association Rules, Mining Multidimensional Association Rules from Relational Databases, Summary, Review Questions.
     
    UNIT–3
    5. Classification and Prediction
    Objectives, Introduction, Learning, Classification, Classification: A Complete Definition, Issues Regarding Classification and Prediction, Comparison between Classification and Prediction Methods, Classification by Decision Tree Induction, Decision Tree Induction, Bayesian Classification, Baye’s Theorem and Bayesian Probability, Naive Bayes Classifiers, Bayesian Belief Networks, Training Bayesian Belief Networks, Neural Networks, A Neural Net, Back Propagation, Multi-layer Feed-Forward Networks, Different Classification Methods, k-Nearest Neighbor Classifiers, Genetic Algorithm, Summary, Review Questions.
     
    6. Cluster Analysis
    Objectives, Introduction, Data Types in Cluster Analysis, Interval-Scaled Variables, Binary Variables, Categorical, Ordinal and Ratio-scaled Variables, Variables of Mixed Types, Vector Objects, Categories of Major Clustering Methods, Partitioning Methods, Classical Partitioning Methods, Hierarchical Clustering Methods, Density-Based Methods, DBSCAN, OPTICS: Ordering Points to Identify the Clustering Structure, Grid-Based Methods, STING: Statistical Information Grid, CLIQUE, Modal Based Method, Statistical Approaches, Outlier Analysis, Summary, Review Questions.
     
    UNIT–4
    7. Data Warehousing
    Objectives, Introduction, Definition, Database vs. Data Warehouse, Characteristics of a Data Warehouse, Data Warehouse Delivery Method, IT Strategy, Business Case, Education and Prototyping, Business Requirements, Technical Blueprint, Building the Vision, History Load, Ad-hoc Query, Automation, Extending Scope, Evolution of Requirements, A Multidimensional Data Model, Basic Concepts, Data Cube, Schemas for Multidimensional Databases, Star Schema, Snowflake Schema, Fact Constellation, Example for Above Explanation, Concept Hierarchies, Process Architecture, Load Manager, Load Manager Architecture, Extract Data from Source, Fast Load, Simple Transformation, Warehouse Manager, Warehouse Manager Architecture, Complex Transformations, Transform into a Starflake Schema, Create Indexes and Views, Generate the Summaries, Query Manager, Three Tier Architecture of Data Warehousing, Warehouse Database Server [Bottom Tier], OLAP Server [Middle Tier], Front-End Client Layer [Top Tier], Data Marts, Data Mining, Summary, Review Questions.
     
    UNIT–5
    8. Aggregation
    Objectives, Introduction, Historical Information, Query Facility, OLAP On-Line Analytical Processing, OLAP Operations in the Multidimensional Data Model, OLAP Servers, ROLAP, MOLAP, HOLAP, DOLAP, Data Mining Interface, Security and Security Requirements, User Access, Legal Requirements, Audit Requirements, Network Requirements, Data Movement, Documentation, High-Security Environments, Performance Impact of Security, Views, Data Movement, Auditing, Security Impact on Design, Application Development, Database Design, Testing, Backup and Recovery, Definitions, Hardware, Software, Backup Strategies, Testing The Strategy, Disaster Recovery, Tuning the Data Warehouse, Assessing Performance, Tuning the Data Load, Tuning Queries, Testing the Data Warehouse, Developing the Test Plan, Testing Backup Recovery, Testing the Operational, Testing the Database, Summary, Review Questions.
     
    P. Paper

Reviews of Data Mining and Warehousing

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komal gupta

04 Sep 2013

about the book

every thing is very clearly explained in this book

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