A Data Mining Architecture

Advances in K-means Clustering: A Data Mining Thinking  

Posted by ChrisRedfield at May 13, 2015
Advances in K-means Clustering: A Data Mining Thinking

Junjie Wu - Advances in K-means Clustering: A Data Mining Thinking
Published: 2012-07-10 | ISBN: 3642298060, 3642447570 | PDF | 180 pages | 3.1 MB

A Data Mining Algorithms: Explained Using R (repost)  

Posted by arundhati at April 2, 2015
A Data Mining Algorithms: Explained Using R (repost)

Pawel Cichosz, "A Data Mining Algorithms: Explained Using R"
2015 | ISBN-10: 111833258X | 720 pages | PDF | 6 MB

A Data Mining Algorithms: Explained Using R  

Posted by roxul at Nov. 15, 2014
A Data Mining Algorithms: Explained Using R

Pawel Cichosz, "A Data Mining Algorithms: Explained Using R"
English | ISBN: 111833258X | 2015 | 720 pages | PDF | 6 MB

Quality Measures in Data Mining [Repost]  eBooks & eLearning

Posted by tanas.olesya at April 11, 2016
Quality Measures in Data Mining [Repost]

Quality Measures in Data Mining by Fabrice Guillet
English | 8 Jan. 2007 | ISBN: 3540449116 | 315 Pages | PDF | 6 MB

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst.

Data Preprocessing in Data Mining  

Posted by interes at Nov. 14, 2014
Data Preprocessing in Data Mining

Data Preprocessing in Data Mining (Intelligent Systems Reference Library) by Salvador García and Julián Luengo
English | 2014 | ISBN: 331910246X | 320 pages | PDF | 8 MB

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process.
Fabrice Guillet, Fabrice Guillet;Howard J. Hamilton, Quality Measures in Data Mining (Repost)

Fabrice Guillet, Fabrice Guillet;Howard J. Hamilton, Quality Measures in Data Mining
ISBN: 3540449116 | edition 2007 | PDF | 315 pages | 6 mb

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences.

Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach  eBooks & eLearning

Posted by karapuzik at Nov. 30, 2009
Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach

Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach
187 pages | Springer; 1 edition (November 5, 2009) | 3642025404 | PDF | 1 Mb

Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters.
Data Mining and Knowledge Discovery in Real Life Applications

Data Mining and Knowledge Discovery in Real Life Applications
Publisher: IN-TECH | ISBN: 3902613530 | edition 2009 | PDF | 464 pages | 20 mb

This book presents four different ways of theoretical and practical advances and applications of data mining in different promising areas like Industrialist, Biological, and Social. This book will serve as a Data Mining bible to show a right way for the students, researchers and practitioners in their studies.

Quality Measures in Data Mining  eBooks & eLearning

Posted by maxxum at Feb. 3, 2007
136190
Fabrice Guillet, Howard J. Hamilton, «Quality Measures in Data Mining»
Springer | ISBN 3540449116 | February 2007 | PDF | 5.5 Mb | 313 pages

Data mining analyzes large amounts of data to discover knowledge relevant to decision making. Typically, numerous pieces of knowledge are extracted by a data mining system and presented to a human user, who may be a decision-maker or a data-analyst. The user is confronted with the task of selecting the pieces of knowledge that are of the highest quality or interest according to his or her preferences. Since this selection is sometimes a daunting task, designing quality and interestingness measures has become an important challenge for data mining researchers in the last decade.

Predictive Data Mining Models  eBooks & eLearning

Posted by naag at Dec. 4, 2016
Predictive Data Mining Models

Predictive Data Mining Models (Computational Risk Management)
Springer | 5 Oct 2016 | English | ISBN: 9811025428 | 102 pages | PDF | 4 Mb