Encyclopedia of Data Warehousing and Mining, 2nd Edition

  • Published By:
  • ISBN-10: 1605660116
  • ISBN-13: 9781605660110
  • Grade Level Range: College Freshman - College Senior
  • 2396 Pages | eBook
  • Original Copyright 2008 | Published/Released November 2008
  • This publication's content originally published in print form: 2008

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About

Overview

Offers a thorough examination of the issues of importance in the rapidly changing field of data warehousing and mining. Includes numerous entries on theories, methodologies, functionalities, and applications.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Editorial Page.
List of Contributors.
Contents by Volume.
Contents by Topic.
Foreword.
Preface.
Acknowledgment.
About the Editor.
1: Action Rules Mining.
2: Active Learning with Multiple Views.
3: Adaptive Web Presence and Evolution through Web Log Analysis.
4: Aligning the Warehouse and the Web.
5: Analytical Competition for Managing Customer Relations.
6: Analytical Knowledge Warehousing for Business Intelligence.
7: Anomaly Detection for Inferring Social Structure.
8: The Application of Data-Mining to Recommender Systems.
9: Applications of Kernel Methods.
10: Architecture for Symbolic Object Warehouse.
11: Association Bundle Identification.
12: Association Rule Hiding Methods.
13: Association Rule Mining.
14: On Association Rule Mining for the QSAR Problem.
15: Association Rule Mining of Relational Data.
16: Association Rules and Statistics.
17: Audio and Speech Processing for Data Mining.
18: Audio Indexing.
19: An Automatic Data Warehouse Conceptual Design Approach.
20: Automatic Genre-Specific Text Classification.
21: Automatic Music Timbre Indexing.
22: A Bayesian Based Machine Learning Application to Task Analysis.
23: Behavioral Pattern-Based Customer Segmentation.
24: Best Practices in Data Warehousing.
25: Bibliomining for Library Decision-Making.
26: Bioinformatics and Computational Biology.
27: Biological Image Analysis via Matrix Approximation.
28: Bitmap Join Indexes vs. Data Partitioning.
29: Bridging Taxonomic Semantics to Accurate Hierarchical Classification.
30: A Case Study of a Data Warehouse in the Finnish Police.
31: Classification and Regression Trees.
32: Classification Methods.
33: Classification of Graph Structures.
34: Classifying Two-Class Chinese Texts in Two Steps.
35: Cluster Analysis for Outlier Detection.
36: Cluster Analysis in Fitting Mixtures of Curves.
37: Cluster Analysis with General Latent Class Model.
38: Cluster Validation.
39: Clustering Analysis of Data with High Dimensionality.
40: Clustering Categorical Data with K-Modes.
41: Clustering Data in Peer-to-Peer Systems.
42: Clustering of Time Series Data.
43: On Clustering Techniques.
44: Comparing Four-Selected Data Mining Software.
45: Compression-Based Data Mining.
46: Computation of OLAP Data Cubes.
47: Conceptual Modeling for Data Warehouse and OLAP Applications.
48: Constrained Data Mining.
49: Constraint-Based Association Rule Mining.
50: Constraint-Based Pattern Discovery.
51: Context-Driven Decision Mining.
52: Context—Sensitive Attribute Evaluation.
53: Control-Based Database Tuning under Dynamic Workloads.
54: Cost-Sensitive Learning.
55: Count Models for Software Quality Estimation.
56: Data Analysis for Oil Production Prediction.
57: Data Confidentiality and Chase-Based Knowledge Discovery.
58: Data Cube Compression Techniques: A Theoretical Review.
59: A Data Distribution View of Clustering Algorithms.
60: Data Driven vs. Metric Driven Data Warehouse Design.
61: Data Mining and Privacy.
62: Data Mining and the Text Categorization Framework.
63: Data Mining Applications in Steel Industry.
64: Data Mining Applications in the Hospitality Industry.
65: Data Mining for Fraud Detection System.
66: Data Mining for Improving Manufacturing Processes.
67: Data Mining for Internationalization.
68: Data Mining for Lifetime Value Estimation.
69: Data Mining for Model Identification.
70: Data Mining for Obtaining Secure E-Mail Communications.
71: Data Mining for Structural Health Monitoring.
72: Data Mining for the Chemical Process Industry.
73: Data Mining in Genome Wide Association Studies.
74: Data Mining in Protein Identification by Tandem Mass Spectrometry.
75: Data Mining in Security Applications.
76: Data Mining in the Telecommunications Industry.
77: Data Mining Lessons Learned in the Federal Government.
78: A Data Mining Methodology for Product Family Design.
79: Data Mining on XML Data.
80: Data Mining Tool Selection.
81: Data Mining with Cubegrades.
82: Data Mining with Incomplete Data.
83: Data Pattern Tutor for AprioriAll and PrefixSpan.
84: Data Preparation for Data Mining.