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Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules.

Data Mining and Statistics for Decision Making : Stephane Tuffery :

They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

Preface xvii Foreword xxi Foreword from the French language edition xxiii List of trademarks xxv 1 Overview of data mining 1 24 1. Practical Sql Anthony Debarros.

Data Mining and Statistics for Decision Making

Blockchain Basics Daniel Drescher. Principles and best practices of scalable realtime data systems James Warren. Text Mining with R David Robinson. Marketing Analytics Wayne L. Machine Learning Stephen Marsland. Making Data Visual Miriah Meyer.

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R in Action Robert I. Data Smart John W. High Performance Spark Rachel Warren. Successful Business Intelligence Cindi Howson. Data Mining Eibe Frank. Data Analysis with R Tony Fischetti. Big Data Fundamentals Wajid Khattak. Advanced Analytics with Spark Josh Wills. Principles of Database Management Wilfried Lemahieu. Deep Learning Cookbook Douwe Osinga. Data-Driven Storytelling Nicholas Diakopoulos. Introduction to Data Mining: Pandas for Everyone Daniel Y.

Data Analytics Herbert Jones. Web Data Mining Bing Liu.


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Fundamentals of Deep Learning Nikhil Buduma. Other books in this series. Graphical Models Matthias Steinbrecher.


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  7. Symbolic Data Analysis Lynne Billard. Data mining isusually associated with a business or an organization's need toidentify trends and profiles, allowing, for example, retailers todiscover patterns on which to base marketing objectives. This book looks at both classical and modern methods of datamining, such as clustering, discriminate analysis, decision trees, neural networks and support vector machines along with illustrativeexamples throughout the book to explain the theory of these models. Recent methods such as bagging and boosting, decision trees, neuralnetworks, support vector machines and genetic algorithm are alsodiscussed along with their advantages and disadvantages.

    Presents a comprehensive introduction to all techniques usedin data mining and statistical learning. Gives practical tips for data mining implementation as well asthe latest techniques and state of the art theory.

    Stanford Statistical Data Mining

    Looks at a range of methods, tools and applications, such asscoring to web mining and text mining and presents their advantagesand disadvantages. Supported by an accompanying website hosting datasets and useranalysis.

    Data Mining and Statistics for Decision Making

    Business intelligence analysts and statisticians, compliance andfinancial experts in both commercial and government organizationsacross all industry sectors will benefit from this book. Table of contents Preface. Foreword from the French language edition. Oveview of data mining.