An overview of the multidisciplinary field of data mining, this book focuses specifically on new methodologies and case studies. Included are case studies written by 44 leading scientists and talented young scholars from seven different countries. Topics covered include data mining based on rough sets, the impact of missing data, and mining free text for structure. In addition, the four basic mining operations supported by numerous mining techniques are addressed: predictive model creation supported by supervised induction techniques; link analysis supported by association discovery and sequence discovery techniques; DB segmentation supported by clustering techniques; and deviation detection supported by statistical techniques.
| |||||||||||||||
Book Description
Editorial Reviews
About the Author
John Wang is a professor in the department of information and decision sciences at Montclair State University, has a Ph.D. in operations research from Temple University, and has worked as an assistant professor at Beijing University of Sciences and Technology. He has served as a referee for Operations Research and IEEE Transactions on Control Systems Technology. His current research interests include optimization, nonlinear programming, and manufacturing systems engineering. He lives in Upper Montclair, New Jersey.
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
1 of 2 people found the following review helpful:
4.0 out of 5 stars
Fresh Insights!,
By Steve McCarty (Kagawa Junior College, Japan) - See all my reviews
This review is from: Data Mining: Opportunities and Challenges (Hardcover)
I have read this book with growing interest - this is the first major comprehensive and current introduction to data mining (DM) in ten years. Extremely interesting and useful book! It contains a collection of 20 high quality articles written by experts in data mining (DM) and knowledge discovery (KDD) from the following countries: Argentina, Canada, Finland, Italy, South Africa, Sweden, Taiwan, and USA. The book is filled with fresh insights on data mining: it provides a complete overview of DM-technology and outlines how it can be applied to real world problems and applications.
1 of 2 people found the following review helpful:
4.0 out of 5 stars
Valuable guide into the field of Data Mining,
By Steve McCarty (Kagawa Junior College, Japan) - See all my reviews
This review is from: Data Mining: Opportunities and Challenges (Hardcover)
This book is a very valuable guide into the field of Data Mining. Addressing theoretical issues and tools from Bayesian Reasoning through Rough Sets to Self-Organizing Maps along with a penetrating look at applications from HealthCare to Banking and Finances, it allows the reader to become acquainted with the state-of-the-art in Data Mining by a group of eminent specialists in this area. It will guide the reader directly to the hearth of the rich world of theory and applications of Data Mining. I am confident that it will become a good companion to any researcher and student in this field.
0 of 1 people found the following review helpful:
4.0 out of 5 stars
Useful to a wide and varied audience!,
By Steve McCarty (Kagawa Junior College, Japan) - See all my reviews
This review is from: Data Mining: Opportunities and Challenges (Hardcover)
This book is a collection of the latest thinking in the area of data mining. The theoretical discussions would be useful to the initiated reader and the cases and experiments are excellent pointers for practitioners.
Share your thoughts with other customers: Create your own review
|
|
Inside This Book
(learn more)
Browse and search another edition of this book.
Browse and search another edition of this book.
First Sentence:
Data acquired for analysis can have many different forms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
document cluster map, rule set computed, dominating neurons, addressing missing data, document structure information, word cluster map, heterogeneous time series, attribute subset selection, basic cost models, hierarchical mixture models, vertical density representation, selforganizing maps, global coverings, feedforward layer, rough set model, costing situation, composite learning, farm skeleton, rough set theory, extractor module, recurrent layer, sequence manager, visual data mining, rough set approach, router agent Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, International Conference, Loan Application, Morgan Kaufmann, San Francisco, Computer Science, World Wide Web, Lecture Notes, Menlo Park, Data Engineering, Journal of the American Statistical Association, San Mateo, Academic Press, John Wiley, Operating Margin, Prentice Hall, Tab Tab, Markov Chain Monte Carlo, Upper Saddle River, Attribute Value Test, Cost Center, Stora Enso, United States, Collecting Agent, Fair Credit Reporting Act New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Data acquired for analysis can have many different forms. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
document cluster map, rule set computed, dominating neurons, addressing missing data, document structure information, word cluster map, heterogeneous time series, attribute subset selection, basic cost models, hierarchical mixture models, vertical density representation, selforganizing maps, global coverings, feedforward layer, rough set model, costing situation, composite learning, farm skeleton, rough set theory, extractor module, recurrent layer, sequence manager, visual data mining, rough set approach, router agent Key Phrases - Capitalized Phrases (CAPs): (learn more)
New York, International Conference, Loan Application, Morgan Kaufmann, San Francisco, Computer Science, World Wide Web, Lecture Notes, Menlo Park, Data Engineering, Journal of the American Statistical Association, San Mateo, Academic Press, John Wiley, Operating Margin, Prentice Hall, Tab Tab, Markov Chain Monte Carlo, Upper Saddle River, Attribute Value Test, Cost Center, Stora Enso, United States, Collecting Agent, Fair Credit Reporting Act New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 23
books:
See all 23 books this book cites
30
books
cite this book:
See all 30 books citing this book
- Advanced Topics in Information Resources Management Series, Vol. 1 by Mehdi Khosrowpour in Front Matter
- Research Issues in Data Engineering (Ride 2000): 10 International Workshop by IEEE on page 322
- Architectural Issues of Web-Enabled Electronic Business by Nansi Shi in Front Matter
- Advanced Topics in End User Computing, Vol. 3 by Mo Adam Mahmood in Front Matter
- Markov Chain Monte Carlo in Practice (Chapman & Hall/CRC Interdisciplinary Statistics) by W.R. Gilks on page 2
See all 23 books this book cites
- Cognitive Systems - Information Processing Meets Brain Science by Richard G.M. Morris on page 257
- Architectural Issues of Web-Enabled Electronic Business by Nansi Shi in Front Matter
- Adaptive Evolutionary Information Systems by Nandish V. Patel in Front Matter
- Web-Enabled Systems Integration: Practices and Challenges by Ajantha Dahanayake in Front Matter
- The Economic and Social Impacts of E-Commerce by Sam Lubbe in Front Matter
See all 30 books citing this book
Books on Related Topics
(learn more)
|
Tag this product(What's this?)Think of a tag as a keyword or label you consider is strongly related to this product.
Tags will help all customers organize and find favorite items. |
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
