Statistical Analysis and Data Mining Applications

Di Robert Nisbet, John Elder e Gary Miner

864 pagine, Copertina Rigida
Dimensioni: 191 X 235 mm
Lingua Inglese
Copyright 2009
EUR 80.00 + IVA

Elementi Chiave

Scritto "Da Professionisti per Professionisti"

• Spiegazione dei concetti semplice per un'immediata comprensione
• Numerosi tutorial che forniscono istruzioni passo-passo per la creazione di modelli utilizzando STATISTICA, SAS e SPSS
• Consigli pratici derivati da numerose applicazioni di successo nel mondo reale
• Include diversi casi studio, esempi, diapositive di MS PowerPoint e data set
• CD-DVD allegato contenente il software "Data Miner - QC-Miner - Text Miner" completamente funzionante e valutabile per 90 giorni.

Descrizione

Questo manuale è una guida di riferimento completa per condurre gli analisti, gli scienziati, i tecnici o i ricercatori attraverso tutte le fasi dell'analisi dei dati, in quanto aiuta a scoprire i problemi tecnici e di business, a comprendere la potenza e le debolezze dei moderni algoritmi di data mining, e ad impiegare i metodi statistici più adeguati alle situazioni reali presenti. Questo libro è stato pensato per affrontare dei complessi data set di grandi dimensioni con le nuove metodologie statistiche, e per essere in grado di valutare oggettivamente le analisi e le soluzioni. Fornisce delle spiegazioni chiare ed intuitive dei principi e degli strumenti per la risoluzione dei problemi, utilizzando tecniche analitiche all'avanguardia che venogno presenate applicandole a problemi reali, in modo da evidenziare le loro funzionalità ai professionisti che operano in tutti i settori aziendali. Il manuale unisce, in un'unica risorsa, tutte le informazioni necessarie ad una persona che si avvicina per la prima volta a queste tematiche, con i consigli sull'utilizzo del data mining per la ricerca di soluzioni ottimali.

Citazioni

"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here."

"Going beyond its responsibility as a reference book, this resource also provides detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success."

"If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner."

- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World

"Great introduction to the real-world process of data mining. The overviews, practical advise, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners."

- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)

Sommario

Foreword (Dean Abbott and Tony Lachenbruch)
Preface
Introduction
List of Tutorials by Guest Authors

PART I: History of Phases of Data Analysis, Basic Theory, and the Data Mining Process
Chapter 1. The Background for Data Mining Practice
Chapter 2. Theoretical Considerations for Data Mining
Chapter 3. The Data Mining Process
Chapter 4. Data Understanding and Preparation
Chapter 5. Feature Selection
Chapter 6: Accessory Tools for Doing Data Mining

PART II: - The Algorithms in Data Mining and Text Mining: the Organization of the Three Most Common Data Mining Tools, and Selected Specialized Areas Using Data Mining
Chapter 7. Basic Algorithms for Data Mining: A Brief Overview
Chapter 8: Advanced Algorithms for Data Mining
Chapter 9. Text Mining and Natural Language Processing
Chapter 10. The Three Most Common Data Mining Software Tools
Chapter 11. Classification
Chapter 12. Numerical Prediction
Chapter 13. Model Evaluation and Enhancement
Chapter 14. Medical Informatics
Chapter 15. Bioinformatics
Chapter 16. Customer Response Modeling
Chapter 17. Fraud Detection

PART III: Tutorials - Step-by-Step Case Studies as a Starting Point to Learn How to Do Data Mining Analyses
Guest Authors of the Tutorials
A. How to use the Data Miner Recipe
B. Data Mining for Aviation Safety
C. Predictive Movie Box-Office Receipts
D. Detecting Unsatisfied Customersi: A Case Study
E. Credit Scoring
F. Churn Analysis
G. Text Mining: Automobile Brand Review
H. Predictive Process Control: QC-Data Mining
I. Business Administration in a Medical Industry
J. Clinical Psychology: Making Decisions about Best Therapy fora a Client
K. Education – Leadership Training for Business and Education
L. Dentistry: Facial Pain Study
M Profit Analysis of the German Credit Data

PART IV: Measuring True Complexity, the "Right Model for the Right Use”, Top Mistakes, and the Future of Analytics.
Chapter 18: Model Complexity (and How Ensembles Help)
Chapter 19: The Right Model for the Right Purpose: When Less Is Good Enough
Chapter 20: The Top 10 Data Mining Mistakes
Chapter 21: Prospect for the Future of Data Mining and Text Mining as Part of Our Everyday Lives
Chapter 22: Summary: Our Design

GLOSSARY of STATISICAL and DATA MINING TERMS
INDEX
DVD Install Istructions
CD – With Additional Tutorials, data sets, Power Points, and Data Mining software (STATISTICA Data Miner & Text Miner & QC-Miner – 90 day free trial)

Informazioni sugli Autori

Rober Nisbet, Pacific Capital Bank Corporation, Santa Barbara, CA, USA; John Elder, IV, Elder Research, Inc. and the University of Virginia, Charlottesville, USA e Gary Miner, StatSoft, Inc., Tulsa, OK, USA

Se siete interessati ad acquistare questo manuale potete contattare StatSoft Italia allo 049.8934654 o all'indirizzo email info@statsoft.it.