Ebook Free Practical Applications Of Data Mining, by Sang C. Suh
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Practical Applications Of Data Mining, by Sang C. Suh
Ebook Free Practical Applications Of Data Mining, by Sang C. Suh
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Practical Applications of Data Mining emphasizes both theory and applications of data mining algorithms. Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples. This book will help any database and IT professional understand how to apply data mining techniques to real-world problems. Following an introduction to data mining principles, Practical Applications of Data Mining introduces association rules to describe the generation of rules as the first step in data mining. It covers classification and clustering methods to show how data can be classified to retrieve information from data. Statistical functions and drough set theory are discussed to demonstrate how statistical and rough set formulas can be used for data analytics and knowlege discovery. Neural networks is an important branch in computational intelligence. It is introduced and explored in the text to investigate the role of neural network algorithms in data analytics.
- Sales Rank: #1138676 in Books
- Brand: Brand: Jones Bartlett Learning
- Published on: 2011-02-03
- Original language: English
- Number of items: 1
- Dimensions: 9.10" h x 1.10" w x 7.40" l, 1.58 pounds
- Binding: Paperback
- 420 pages
- Used Book in Good Condition
Most helpful customer reviews
1 of 1 people found the following review helpful.
Getting NEW Knowledge from an old database
By John Matlock
Data mining came about when people in IT departments began to realize that in their corporate databases there was a lot of knowledge that was sitting there, basically unused. An easy example:
You run two ads for a mail order item. Ad "A" pulls five orders. Ad "B" pulls 1 order. You put these results into an Ad-Result database, analysis subsequently shows that Ad "A" is obviously better, right? But suppose on further analysis you find that the sole customer from Ad "B" ordered ten more items. But this information is off in the Orders Placed database, not in the ad results database. Integrating "Knowledge" from both databases gives you much better information. This is
Data Mining, or a subset (some people say a superset) thereof called Knowledge Discovery in Databases (KDD).
There are applications for such procedures in nearly all forms of doing business: Order a book from Amazon and on your next visit they will suggest some other titles you might like. Do a search on Google and they will remember your search and perhaps suggest some other things you might like to see. In the health care or life insurance field you would like to know about the effects of smoking, overweight, exercise, family history and more on longevity.
This book is a description of how to do practical applications by using statistics, set and Bayes theories, neural networks, and Fuzzy information retrieval where you don't quite know what to search for in the database. Some background in these areas would be helpful, but I found that using what I knew got me started and other things came along in time. A well done book.
0 of 3 people found the following review helpful.
Fantastic
By Victicom
The process of buying this book was fantastic. The book was delivered on time, for a very good price, in mint condition.
I would definitely buy again from the seller.
Notes about the book: The book is good, though it assumes you have some background knowledge of some statistics, and algorithms which they use.Some times you have to go in, and manipulate the data sets to understand what they are trying to say, but you still get the general idea. Any further input on the book would be beyond the scope of this review.
I am pleased with the purchase.
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