Encyclopedia of Data Warehousing and Mining, 1st Edition

  • Editor: John Wang [Montclair State University]
  • Published By:
  • ISBN-10: 1591405599
  • ISBN-13: 9781591405597
  • Grade Level Range: College Freshman - College Senior
  • 1248 Pages | eBook
  • Original Copyright 2006 | Published/Released November 2005
  • This publication's content originally published in print form: 2006

  • Price:  Sign in for price

About

Overview

Data warehousing and mining is the science of managing and analyzing large datasets and discovering novel patterns and in recent years has emerged as a particularly exciting and industrially relevant area of research. Prodigious amounts of data are now being generated in domains as diverse as market research, functional genomics and pharmaceuticals; intelligently analyzing these data, with the aim of answering crucial questions and helping make informed decisions, is the challenge that lies ahead.

The Encyclopedia of Data Warehousing and Mining provides a comprehensive, critical and descriptive examination of concepts, issues, trends, and challenges in this rapidly expanding field of data warehousing and mining. This encyclopedia consists of more than 350 contributors from 32 countries, 1,800 terms and definitions, and more than 4,400 references. This authoritative publication offers in-depth coverage of evolutions, theories, methodologies, functionalities, and applications of data warehousing and mining in such interdisciplinary industries as healthcare informatics, artificial intelligence, financial modeling, and applied statistics, making it a single source of knowledge and latest discoveries in the field of data warehousing and mining.

Key features include:

  • Contributions from international scholars providing comprehensive coverage of theories and concepts
  • Example of best practices and case studies
  • A renowned and experienced Editorial Advisory Board
  • A compendium of more than 1,800 terms, definitions and explanations of concepts, processes and acronyms provided by international experts
  • More than 4,400 comprehensive references
  • Organized by titles and indexed by authors and topics, making it a convenient methods of reference for readers
  • Cross referencing of key terms, figures and information

Originally published in print format in 2005.

Contributors

Contributors

  • John Wang [Montclair State University]

Table of Contents

Cover.
Title Page.
Copyright Page.
Editorial Advisory Board.
List of Contributors.
Contents.
Foreword.
Preface.
Acknowledgments.
About the Editor.
1: Action Rules.
2: Active Disks for Data Mining.
3: Active Learning with Multiple Views.
4: Administering and Managing a Data Warehouse.
5: Agent–Based Mining of User Profiles for E–Services.
6: Aggregate Query Rewriting in Multidimensional Databases.
7: Aggregation for Predictive Modeling with Relational Data.
8: API Standardization Efforts for Data Mining.
9: The Application of Data Mining to Recommender Systems.
10: Approximate Range Queries by Histograms in OLAP.
11: Artificial Neural Networks for Prediction.
12: Association Rule Mining.
13: Association Rule Mining and Application to MPIS.
14: Association Rule Mining of Relational Data.
15: Association Rules and Statistics.
16: Automated Anomaly Detection.
17: Automatic Musical Instrument Sound Classification.
18: Bayesian Networks.
19: Best Practices in Data Warehousing from the Federal Perspective.
20: Bibliomining for Library Decision–Making.
21: Biomedical Data Mining Using RBF Neural Networks.
22: Building Empirical–Based Knowledge for Design Recovery.
23: Business Processes.
24: Case–Based Recommender Systems.
25: Categorization Process and Data Mining.
26: Center–Based Clustering and Regression Clustering.
27: Classification and Regression Trees.
28: Classification Methods.
29: Closed–Itemset Incremental–Mining Problem.
30: Cluster Analysis in Fitting Mixtures of Curves.
31: Clustering Analysis and Algorithms.
32: Clustering in the Identification of Space Models.
33: Clustering of Time Series Data.
34: Clustering Techniques.
35: Clustering Techniques for Outlier Detection.
36: Combining Induction Methods with the Multimethod Approach.
37: Comprehensibility of Data Mining Algorithms.
38: Computation of OLAP Cubes.
39: Concept Drift.
40: Condensed Representations for Data Mining.
41: Content–Based Image Retrieval.
42: Continuous Auditing and Data Mining.
43: Data Driven vs. Metric Driven Data Warehouse Design.
44: Data Management in Three–Dimensional Structures.
45: Data Mining and Decision Support for Business and Science.
46: Data Mining and Warehousing in Pharma Industry.
47: Data Mining for Damage Detection in Engineering Structures.
48: Data Mining for Intrusion Detection.
49: Data Mining in Diabetes Diagnosis and Detection.
50: Data Mining in Human Resources.
51: Data Mining in the Federal Government.
52: Data Mining in the Soft Computing Paradigm.
53: Data Mining Medical Digital Libraries.
54: Data Mining Methods for Microarray Data Analysis.
55: Data Mining with Cubegrades.
56: Data Mining with Incomplete Data.
57: Data Quality in Cooperative Information Systems.
58: Data Quality in Data Warehouses.
59: Data Reduction and Compression in Database Systems.
60: Data Warehouse Back–End Tools.
61: Data Warehouse Performance.
62: Data Warehousing and Mining in Supply Chains.
63: Data Warehousing Search Engine.
64: Data Warehousing Solutions for Reporting Problems.
65: Database Queries, Data Mining, and OLAP.
66: Database Sampling for Data Mining.
67: DEA Evaluation of Performance of E-Business Initiatives.
68: Decision Tree Induction.
69: Diabetic Data Warehouses.
70: Discovering an Effective Measure in Data Mining.
71: Discovering Knowledge from XML Documents.
72: Discovering Ranking Functions for Information Retrieval.
73: Discovering Unknown Patterns in Free Text.
74: Discovery Informatics.
75: Discretization for Data Mining.
76: Discretization of Continuous Attributes.
77: Distributed Association Rule Mining.
78: Distributed Data Management of Daily Car Pooling Problems.
79: Drawing Representative Samples from Large Databases.
80: Efficient Computation of Data Cubes and Aggregate Views.
81: Embedding Bayesian Networks in Sensor Grids.
82: Employing Neural Networks in Data Mining.
83: Enhancing Web Search through Query Log Mining.
84: Enhancing Web Search through Web Structure Mining.
85: Ensemble Data Mining Methods.
86: Ethics of Data Mining.
87: Ethnography to Define Requirements and Data Model.
88: Evaluation of Data Mining Methods.
89: Evolution of Data Cube Computational Approaches.
90: Evolutionary Computation and Genetic Algorithms.
91: Evolutionary Data Mining for Genomics.
92: Evolutionary Mining of Rule Ensembles.
93: Explanation–Oriented Data Mining.
94: Factor Analysis in Data Mining.
95: Financial Ratio Selection for Distress Classification.
96: Flexible Mining of Association Rules.
97: Formal Concept Analysis Based Clustering.
98: Fuzzy Information and Data Analysis.
99: A General Model for Data Warehouses.
100: Genetic Programming.
101: Graph Transformations and Neural Networks.
102: Graph–Based Data Mining.
103: Group Pattern Discovery Systems for Multiple Data Sources.
104: Heterogeneous Gene Data for Classifying Tumors.
105: Hierarchical Document Clustering.
106: High Frequency Patterns in Data Mining.
107: Homeland Security Data Mining and Link Analysis.
108: Humanities Data Warehousing.
109: Hyperbolic Space for Interactive Visualization.