Frissebb adatbányász könyvek az amazon.com-on.

1.
The Top Ten Algorithms in Data Mining
Price: 64 USD
Hardcover: 232 pages
Publication Date: April 9, 2009
Publisher: Chapman & Hall
CRC Data Mining and Knowledge Discovery Series
Xindong Wu (Editor) - Vipin Kumar (Editor) 

Topics:
* classification
* clustering
* statistical learning
* association analysis
* link mining

Algorithms:
01.C4.5
02.k-Means
03.SVM
04.Apriori
05.EM(=Expectation Maximization)
06.PageRank
07.AdaBoost
08.kNN
09.Naive Bayes
10.CART


2.
PMML in Action
Unleashing the Power of Open Standards for Data Mining and Predictive Analytics

Price: 32 USD
Hardcover: 188 pages
Publication Date: May 18, 2010
Publisher: CreateSpace

ADAPA

Példák adatfile-okkal(zementis.com-on)


3.
Temporal Data Mining

Price: 54 USD
Hardcover:395 pages
Publication Date: March 10, 2010
Publisher: Chapman and Hall/CRC

Szerzői blog


4.
Springer Optimization and Its Applications
Data Mining and Knowledge Discovery via Logic-Based Methods:
Theory, Algorithms, and Application

Price: 124 USD
Hardcover: 350 pages
Publication Date: June 28, 2010, 1st Edition
Publisher:  Springer

This monograph focuses on the development and use of a novel approach, based on mathematical logic.


5.
Synthesis Lectures on Data Mining and Knowledge Discovery
Ensemble Methods in Data Mining:
Improving Accuracy Through Combining Predictions

Price: 28 USD
Hardcover: 126 pages
Publication Date: February 24, 2010
Publisher: Morgan and Claypool Publishers

Ensemble methods combine multiple models into one usually more accurate than the best of its components.

Ensembles can provide a critical boost to industrial challenges
* investment timing
* drug discovery
* fraud detection
* recommendation systems
* where predictive accuracy is more vital than model interpretability.

Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly.

After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms.

The book continues with a clear description of two recent developments:
* Importance Sampling (IS)
* Rule Ensembles (RE).

IS reveals classic ensemble methods
* bagging
* random forests
* boosting

REs->linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles

Snippets of code in R are provided to illustrate the algorithms


6.
Machine Learning: An Algorithmic Perspective

Price: 62 USD
Hardcover: 406 pages
Publication Date: April 1, 2009, 1st Edition
Publisher: Chapman & Hall

The book covers
* neural networks
* graphical models
* reinforcement learning
* evolutionary algorithms
* dimensionality reduction methods
* the important area of optimization