Encyclopedia of Data Warehousing and Mining, Second Edition

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Wang, John
IGI Global, Aug 31, 2008 - Computers - 2542 pages
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There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest.

The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.

 

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Contiene una forma de medir la forma sumar las distancias, la combinacion de kmeans con el kmode

Contents

Comparing FourSelected Data Mining Software
269
CompressionBased Data Mining
278
Computation of OLAP Data Cubes
286
Conceptual Modeling for Data Warehouse and OLAP Applications
293
Constrained Data Mining
301
ConstraintBased Association Rule Mining
307
ConstraintBased Pattern Discovery
313
ContextDriven Decision Mining
320

Architecture for Symbolic Object Warehouse
58
Association Bundle Identification
66
Association Rule Hiding Methods
71
Association Rule Mining
76
On Association Rule Mining for the QSAR Problem
83
Association Rule Mining of Relational Data
87
Association Rules and Statistics
94
Audio and Speech Processing for Data Mining
98
Audio Indexing
104
An Automatic Data Warehouse Conceptual Design Approach
110
Automatic GenreSpecific Text Classification
120
Automatic Music Timbre Indexing
128
A Bayesian Based Machine Learning Application to Task Analysis
133
Behavioral PatternBased Customer Segmentation
140
Best Practices in Data Warehousing
146
Bibliomining for Library DecisionMaking
153
Bioinformatics and Computational Biology
160
Biological Image Analysis via Matrix Approximation
166
Bitmap Join Indexes vs Data Partitioning
171
Bridging Taxonomic Semantics to Accurate Hierarchical Classification
178
A Case Study of a Data Warehouse in the Finnish Police
183
Classification and Regression Trees
192
Classification Methods
196
Classification of Graph Structures
202
Classifying TwoClass Chinese Texts in Two Steps
208
Cluster Analysis for Outlier Detection
214
Cluster Analysis in Fitting Mixtures of Curves
219
Cluster Analysis with General Latent Class Model
225
Cluster Validation
231
Clustering Analysis of Data with High Dimensionality
237
Clustering Categorical Data with kModes
246
Clustering Data in PeertoPeer Systems
251
Clustering of Time Series Data
258
On Clustering Techniques
264
ContextSensitive Attribute Evaluation
328
ControlBased Database Tuning Under Dynamic Workloads
333
CostSensitive Learning
339
Count Models for Software Quality Estimation
346
Data Analysis for Oil Production Prediction
353
Data Confidentiality and ChaseBased Knowledge Discovery
361
A Theoretical Review
367
A Data Distribution View of Clustering Algorithms
374
Data Driven vs Metric Driven Data Warehouse Design
382
Data Mining and Privacy
388
Data Mining and the Text Categorization Framework
394
Data Mining Applications in Steel Industry
400
Data Mining Applications in the Hospitality Industry
406
Data Mining for Fraud Detection System
411
Data Mining for Improving Manufacturing Processes
417
Data Mining for Internationalization
424
Data Mining for Lifetime Value Estimation
431
Data Mining for Model Identification
438
Data Mining for Obtaining Secure EMail Communications
445
Data Mining for Structural Health Monitoring
450
Data Mining for the Chemical Process Industry
458
Data Mining in Genome Wide Association Studies
465
Data Mining in Protein Identification by Tandem Mass Spectrometry
472
Data Mining in Security Applications
479
Data Mining in the Telecommunications Industry
486
Data Mining Lessons Learned in the Federal Government
492
A Data Mining Methodology for Product Family Design
497
Data Mining on XML Data
506
Data Mining Tool Selection
511
Data Mining with Cubegrades
519
Data Mining with Incomplete Data
526
Data Pattern Tutor for AprioriAll and PrefixSpan
531
Data Preparation for Data Mining
538
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About the author (2008)

John Wang is a professor in the Department of Information & Operations Management at Montclair State University, USA. Having received a scholarship award, he came to the USA and completed his PhD in operations research from Temple University. Due to his extraordinary contributions beyond a tenured full professor, Dr. Wang has been honored with a special range adjustment in 2006. He has published over 100 refereed papers and seven books. He has also developed several computer software programs based on his research findings.

He is the Editor-in-Chief of International Journal of Applied Management Science, International Journal of Operations Research and Information Systems, and International Journal of Information Systems and Supply Chain Management. He is the Editor of Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (six-volume) and the Editor of the Encyclopedia of Data Warehousing and Mining, 1st (two-volume) and 2nd (four-volume). His long-term research goal is on the synergy of operations research, data mining and cybernetics. [Editor]

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