PDF Ebook Data Mining: Introductory and Advanced Topics, by Margaret H. Dunham
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Data Mining: Introductory and Advanced Topics, by Margaret H. Dunham
PDF Ebook Data Mining: Introductory and Advanced Topics, by Margaret H. Dunham
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Thorough in its coverage from basic to advanced topics, this book presents the key algorithms and techniques used in data mining. An emphasis is placed on the use of data mining concepts in real world applications with large database components. Includes unique chapters on Web mining, spatial mining, temporal mining, and prototypes and DM products. Separate case studies section highlights real world applications. An excellent reference book for computer database professionals and researchers.
- Sales Rank: #1303478 in Books
- Published on: 2002-09-01
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .70" w x 6.90" l, 1.41 pounds
- Binding: Paperback
- 315 pages
Review
"It is the best book on data mining so far, and I would definitely adopt it for my course. The book is very comprehensive and covers all of the data mining topics and algorithms of which 1 am aware. The depth of coverage of each topic or method is exactly right and appropriate. Each algorithm is presented in pseudocode that is sufficient for any interested readers to convert into a working implementation in a computer language of their choice." — Michael H. Huhns, University of South Carolina
"Discussion on distributed, parallel, and incremental algorithms is outstanding." — Zoran Obradovic, Temple University
From the Back Cover
Margaret Dunham offers the experienced data base professional or graduate level Computer Science student an introduction to the full spectrum of Data Mining concepts and algorithms. Using a database perspective throughout, Professor Dunham examines algorithms, data structures, data types, and complexity of algorithms and space. This text emphasizes the use of data mining concepts in real-world applications with large database components.
KEY FEATURES:- Covers advanced topics such as Web Mining and Spatial/Temporal mining
- Includes succinct coverage of Data Warehousing, OLAP, Multidimensional Data, and Preprocessing
- Provides case studies
- Offers clearly written algorithms to better understand techniques
- Includes a reference on how to use Prototypes and DM products
About the Author
Margaret H. Dunham received the B.A. and the M.S. in mathematics from Miami University in Oxford, Ohio. She earned the Ph.D. degree in computer science from Southern Methodist University. Professor Dunham's research interests encompass main memory databases, data mining, temporal databases, and mobile computing. She is currently an Associate Editor for IEEE Transactions on Knowledge and Data Engineering. She has published numerous technical papers in such research areas as database concurrency control and recovery, database machines, main memory databases, and mobile computing.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Best Data Mining Books i've read.
By Chris
I had to buy several books for my Data mining course. This one is basically a boiled down accumulation of what's in larger books. It covers in 3 pages what larger books take 10-15 to say.
If this is your only book you'll end up having to look up a lot of stuff to understand some of the examples, but I'm really glad I got it and it's a great supplement to learning Data Mining. If I was only going to buy 1 book to study data mining though and use the internet to supplement my learning It would be this one. It's a great book, and everything is laid out well.
It lays things out very well on an abstract level, but the in depth examples may require some additional examples to get down.
0 of 0 people found the following review helpful.
Best Intro Book on Data Mining and Machine Learning...
By Kat'm
This book is concise and easy to use, with about 280 pages; with short examples that you can workout on a piece of paper in the classroom to practice some of the outlined concepts. Don't waste your time buying 600 pages of BS textbooks on machine learning and data mining. One thing I would have wished the authors do, is include a section on Natural Language Processing in regards to text and social media data mining. Otherwise, a very good book, that I ended up keeping it in my home Library for future reference.
1 of 1 people found the following review helpful.
Good introductory survey to various technologies
By British_Bill
The book serves its purpose of providing a introduction to the various technologies that make up data mining. There are three main topic sections. The first gives an overview of the technologies involved such as fuzzy logic, bayesian probability, and neural networks. The second topic area is more concentrated and focuses on how data mining works. This involves utilizing clustering, association, and classification of data. The final section covers advanced topics in web, spatial, and temporal mining. The only complaint that I would have is that most of the coverage at least in section one is cursory and one needs other reference books for serious work in the field. A very strong feature of the book is that pseudocode algorithms are offered in many sections.
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