Handbook of Statistical Analysis and Data Mining Applications
- 10h 51m
- Gary Miner, John Elder, Robert Nisbet
- Elsevier Science and Technology Books, Inc.
- 2009
The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.
- Written "By Practitioners for Practitioners"
- Non-technical explanations build understanding without jargon and equations
- Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models using Statistica, SAS and SPSS software
- Practical advice from successful real-world implementations
- Includes extensive case studies, examples, MS PowerPoint slides and datasets
In this Book
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Foreword 1
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Foreword 2
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Introduction
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List of Tutorials by Guest Authors
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The Background for Data Mining Practice
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Theoretical Considerations for Data Mining
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The Data Mining Process
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Data Understanding and Preparation
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Feature Selection
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Accessory Tools for Doing Data Mining
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Basic Algorithms for Data Mining — A Brief Overview
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Advanced Algorithms for Data Mining
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Text Mining and Natural Language Processing
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The Three Most Common Data Mining Software Tools
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Classification
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Numerical Prediction
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Model Evaluation and Enhancement
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Medical Informatics
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Bioinformatics
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Customer Response Modeling
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Fraud Detection
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Guest Authors of the Tutorials
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How to Use Data Miner Recipe — STATISTICA Data Miner Only
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Data Mining for Aviation Safety — Using Data Mining Recipe “Automatized Data Mining” from STATISTICA
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Predicting Movie Box-Office Receipts — Using SPSS Clementine Data Mining Software
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Detecting Unsatisfied Customers — A Case Study Using SAS Enterprise Miner Version 5.3 for the Analysis
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Credit Scoring — Using STATISTICA Data Miner
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Churn Analysis with SPSS-Clementine
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Text Mining — Automobile Brand Review — Using STATISTICA Data Miner and Text Miner
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Predictive Process Control — QC-Data Mining — Using STATISTICA Data Miner and QC-Miner
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Business Administration in a Medical Industry — Determining Possible Predictors for Days with Hospice Service for Patients with Dementia
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Clinical Psychology — Making Decisions about Best Therapy for a Client — Using Data Mining to Explore the Structure of a Depression Instrument
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Education—Leadership Training for Business and Education — Using C&RT to Predict and Display Possible Structural Relationships
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Dentistry — Facial Pain Study — Based on 84 Predictor Variables (Both Categorical and Continuous)
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Profit Analysis of the German Credit Data — Using SAS-EM Version 5.3
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Predicting Self-Reported Health Status — Using Artificial Neural Networks
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Model Complexity (and How Ensembles Help)
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The Right Model for the Right Purpose—When Less is Good Enough
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Top 10 Data Mining Mistakes
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Prospects for the Future of Data Mining and Text Mining as Part of Our Everyday Lives
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Summary—Our Design
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Glossary
