Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, 1st Edition

  • Published By:
  • ISBN-10: 159904952X
  • ISBN-13: 9781599049526
  • Grade Level Range: College Freshman - College Senior
  • 3699 Pages | eBook
  • Original Copyright 2007 | Published/Released July 2008
  • This publication's content originally published in print form: 2007

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About

Overview

In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

Data Warehousing and Mining: Concepts, Methodologies, Tools and Applications provides the most comprehensive compilation of research available in this emerging and increasingly important field. This six-volume set offers tools, designs, and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data, and online analytical processing. With more than 300 chapters contributed by over 575 experts from around the globe, this authoritative collection will provide libraries with the essential reference on data mining and warehousing.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Page.
List of Contributors.
Contents.
Preface.
Introductory Chapter Data Mining and Data Warehousing.
1: Fundamental Concepts and Theories.
2: Administering and Managing a Data Warehouse.
3: Knowledge Structure and Data Mining Techniques.
4: Physical Data Warehousing Design.
5: Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications.
6: Privacy—Preserving Data Mining on the Web: Foundations and Techniques.
7: Multi—Label Classification: An Overview.
8: Online Data Mining.
9: A Look Back at the PAKDD Data Mining Competition 2006.
10: Introduction to Data Mining in Bioinformatics.
11: Algorithmic Aspects of Protein Threading.
12: A Tutorial on Hierarchical Classification with Applications in Bioinformatics.
13: Introduction to Data Mining and its Applications to Manufacturing.
14: Data Warehousing and OLAP.
15: Data Warehousing, Multi—Dimensional Data Models and OLAP.
16: A Literature Overview of Fuzzy Database Modeling.
17: Conceptual Modeling Solutions for the Data Warehouse.
18: Pattern Comparison in Data Mining: A Survey.
19: Pattern Mining and Clustering on Image Databases.
20: Conceptual Data Modeling Patterns: Representation and Validation.
21: Mining Association Rules in Data Warehouses.
22: Exception Rules in Data Mining.
23: Process—Based Data Mining.
24: Integration of Data Sources through Data Mining.
25: Ensemble Data Mining Methods.
26: Evaluation of Data Mining Methods.
27: Discovering an Effective Measure in Data Mining.
28: Data Warehousing and Data Mining Lessons and EC Companies.
29: Best Practices in Data Warehousing from the Federal Perspective.
30: Decision Support and Data Warehousing: Challenges of a Global Information Environment.
31: An Experimental Replication with Data Warehouse Metrics.
32: Data Warehousing Solutions for Reporting Problems.
33: Development and Design Methodologies.
34: A Multi—Agent Approach to Collaborative Knowledge Production.
35: A Framework for Organizational Data Analysis and Organizational Data Mining.
36: Rule—Based Parsing for Web Data Extraction.
37: Conceptual and Systematic Design Approach for XML Document Warehouses.
38: A Framework for Efficient Association Rule Mining in XML Data.
39: A Methodology for Building XML Data Warehouses.
40: Applying UML for Modeling the Physical Design of Data Warehouses.