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E-raamat: Data Mining: Technologies, Techniques, Tools, and Trends

(The University of Texas at Dallas, USA)
  • Formaat: 288 pages
  • Ilmumisaeg: 23-Jan-2014
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482252507
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  • Formaat: 288 pages
  • Ilmumisaeg: 23-Jan-2014
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781482252507
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Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges.

Three parts divide Data Mining:

Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining

Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information

Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues.

This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence.

Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.
Introduction
1(16)
What is Data Mining?
1(1)
Data Mining Technologies
2(2)
Concepts and Techniques in Data Mining
4(2)
Directions and Trends in Data Mining
6(1)
Organization of this Book
7(3)
Additional Discussion of the Contents
10(4)
How Do We Proceed?
14(3)
Part I. Technologies for Data Mining 17(72)
Introduction to Part I
19(2)
Database Systems
21(28)
Overview
21(1)
Data Models
22(7)
Overview
22(2)
Relational Data Model
24(1)
Entity-Relationship Data Model
25(1)
Object-Oriented Data Model
26(1)
Object Relational Data Model
27(1)
Logic-Based Data Model
28(1)
Architectural Issues
29(3)
Database Design
32(1)
Database Administration
32(1)
Database Management System Functions
33(8)
Overview
33(1)
Query Processing
34(1)
Transaction Management
35(1)
Storage Management
36(1)
Metadata Management
37(1)
Database Integrity
38(1)
Database Security
39(1)
Fault Tolerance
40(1)
Database Systems and Mining
41(6)
Overview
41(1)
Architectural, Modeling, Design and Administration Aspects
42(3)
Data Mining and Database Functions
45(2)
Summary
47(2)
Data Warehousing
49(16)
Overview
49(3)
Technologies for Data Warehousing
52(3)
Developing the Data Warehouse
55(5)
Data Warehousing and Data Mining
60(3)
Summary
63(2)
Some Other Technologies for Data Mining
65(10)
Overview
65(1)
Statistical Reasoning
65(2)
Machine Learning
67(1)
Visualization
68(2)
Parallel Processing
70(1)
Decision Support
71(1)
Summary
72(3)
Architectural Support for Data Mining
75(12)
Overview
75(1)
Integration with Other Technologies
75(2)
Functional Architecture
77(2)
System Architecture
79(5)
Overview
79(1)
Client-Server Technology
80(1)
Relationship to Mining
81(3)
Summary
84(3)
Conclusion to Part I
87(2)
Part II. Techniques and Tools for Data Mining 89(48)
Introduction to Part II
91(2)
The Process of Data Mining
93(12)
Overview
93(1)
Examples
94(2)
Why Data Mining
96(2)
Steps to Data Mining
98(2)
Challenges
100(1)
User Interface Aspects
101(1)
Summary
102(3)
Data Mining Outcomes, Approaches, and Techniques
105(10)
Overview
105(2)
Outcomes of Data Mining
107(2)
Approaches to Data Mining
109(1)
Data Mining Techniques and Algorithms
110(2)
Summary
112(3)
Logic Programming as a Data Mining Technique
115(10)
Overview
115(1)
Deductive Logic Programming
116(2)
Inductive Logic Programming
118(1)
Inductive Logic Programming for Data Mining
119(2)
Applications of Inductive Logic Programming
121(1)
Summary
122(3)
Data Mining Tools
125(10)
Overview
125(1)
Prototypes
126(3)
Overview
126(1)
New Functional Models
127(1)
New Information Services
127(1)
Scalability
128(1)
Understandability of Results
128(1)
Large-Scale Projects
129(1)
Commercial Tools
129(4)
Overview
129(2)
Product 1
131(1)
Product 2
131(1)
Product 3
132(1)
Product 4
132(1)
Product 5
133(1)
Summary
133(2)
Conclusion to Part II
135(2)
Part III. Trends in Data Mining 137(90)
Introduction to Part III
139(2)
Mining Distributed, Heterogeneous, and Legacy Databases
141(16)
Overview
141(1)
Distributed, Heterogeneous, and Legacy Databases Databases
141(5)
Distributed Databases
141(2)
Interoperability of Heterogeneous Database Systems
143(1)
Migrating Legacy Database
144(2)
Mining Distributed, Heterogeneous, and Legacy Databases
146(9)
Summary
155(2)
Multimedia Data Mining
157(22)
Overview
157(1)
Multimedia Databases
158(8)
Architectures for an MM-DBMS
158(3)
Data Modeling
161(2)
Functions of an MM-DBMS
163(3)
Mining Multimedia Data
166(10)
Overview
166(1)
Text Mining
167(3)
Image Mining
170(1)
Video Mining
171(2)
Audio Mining
173(1)
Mining Combinations of Data Types
174(2)
Summary
176(3)
Data Mining and the World Wide Web
179(16)
Overview
179(1)
Internet Database Management and Digital Libraries
180(7)
Technologies
180(1)
Uses of Digital Library
181(1)
Architectural Aspects
182(1)
Database Management Functions
183(4)
Web Data Mining
187(5)
Summary
192(3)
Security and Privacy Issues for Data Mining
195(10)
Overview
195(1)
Background on the Interface Problem
196(1)
Mining, Warehousing, and Inference
197(2)
Inductive Logic Programming and Inference
199(3)
Privacy Issues
202(1)
Summary
203(2)
Metadata Aspects of Mining
205(8)
Overview
205(1)
Background on Metadata
205(2)
Mining and Metadata
207(5)
Summary
212(1)
Conclusion to Part III
213(2)
Summary and Directions
215(12)
About this
Chapter
215(1)
Summary of this Book
215(4)
Challenges in Data Mining
219(2)
Directions for Data Mining
221(1)
Where Do We Go From Here?
222(5)
References 227(6)
Appendices 233(34)
A. Data Management Technology
239(20)
A.1 Overview
241(1)
A.2 Developments in Database Systems
242(5)
A.3 Status, Vision, and Issues
247(2)
A.4 Data Management Systems Framework
249(2)
A.5 Building Information Systems from Framework
251(4)
A.6 Summary
255(1)
A.7 References
256(3)
B. Artificial Intelligence
259(8)
B.1 Overview
259(1)
B.2 Developments in Artificial Intelligence Technologies
259(4)
B.3 Summary
263(1)
B.4 References
263(4)
Index 267


Thuraisingham, Bhavani