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Predictive Data Mining A Practical Guide

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ISBN-10: 1558604030

ISBN-13: 9781558604032

Edition: 1997

Authors: Sholom M. Weiss, Nitin Indurkhya

List price: $78.95
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Description:

Written by Weiss and Indurkhya, this text overviews and recommends tools and methods for extracting new information from data warehouses. It will be of interest to both the artificial intelligence and database communities.
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Book details

List price: $78.95
Copyright year: 1997
Publisher: Elsevier Science & Technology
Publication date: 12/8/1997
Binding: Paperback
Pages: 228
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 0.836
Language: English

Nitin Indurkhya is an associate professor in the School of Computer Science and Engineering at the University of New South Wales in Sydney, Australia. He is also the founder and president of Data-Miner Pty Ltd, which offers education, training, and consulting services in data/text analytics and human language technologies.Before his death, Fred J. Damerau was a researcher at IBM�s Thomas J. Watson Research Center in Yorktown Heights, New York, where he worked on machine learning approaches to natural language processing.

Preface
What Is Data Mining?
Big Data
The Data Warehouse
Timelines
Types of Data-Mining Problems
The Pedigree of Data Mining
Databases
Statistics
Machine Learning
Is Big Better?
Strong Statistical Evaluation
More Intensive Search
More Controlled Experiments
Is Big Necessary?
The Tasks of Predictive Data Mining
Data Preparation
Data Reduction
Data Modeling and Prediction
Case and Solution Analyses
Data Mining: Art or Science?
An Overview of the Book
Bibliographic and Historical Remarks
Statistical Evaluation for Big Data
The Idealized Model
Classical Statistical Comparison and Evaluation
It's Big but Is It Biased?
Objective Versus Survey Data
Significance and Predictive Value
Too Many Comparisons?
Classical Types of Statistical Prediction
Predicting True-or-False: Classification
Error Rates
Forecasting Numbers: Regression
Distance Measures
Measuring Predictive Performance
Independent Testing
Random Training and Testing
How Accurate Is the Error Estimate?
Comparing Results for Error Measures
Ideal or Real-World Sampling?
Training and Testing from Different Time Periods
Too Much Searching and Testing?
Why Are Errors Made?
Bibliographic and Historical Remarks
Preparing the Data
A Standard Form
Standard Measurements
Goals
Data Transformations
Normalizations
Data Smoothing
Differences and Ratios
Missing Data
Time-Dependent Data
Time Series
Composing Features from Time Series
Current Values
Moving Averages
Trends
Seasonal Adjustments
Hybrid Time-Dependent Applications
Multivariate Time Series
Classification and Time Series
Standard Cases with Time-Series Attributes
Text Mining
Bibliographic and Historical Remarks
Data Reduction
Selecting the Best Features
Feature Selection from Means and Variances
Independent Features
Distance-Based Optimal Feature Selection
Heuristic Feature Selection
Principal Components
Feature Selection by Decision Trees
How Many Measured Values?
Reducing and Smoothing Values
Rounding
K-Means Clustering
Class Entropy
How Many Cases?
A Single Sample
Incremental Samples
Average Samples
Specialized Case-Reduction Techniques
Sequential Sampling over Time
Strategic Sampling of Key Events
Adjusting Prevalence
Bibliographic and Historical Remarks
Looking for Solutions
Overview
Math Solutions
Linear Scoring
Nonlinear Scoring: Neural Nets
Advanced Statistical Methods
Distance Solutions
Logic Solutions
Decision Trees
Decision Rules
What Do the Answers Mean?
Is It Safe to Edit Solutions?
Which Solution Is Preferable?
Combining Different Answers
Multiple Prediction Methods
Multiple Samples
Bibliographic and Historical Remarks
What's Best for Data Reduction and Mining?
Let's Analyze Some Real Data
The Experimental Methods
The Empirical Results
Significance Testing
So What Did We Learn?
Feature Selection
Value Reduction
Subsampling or All Cases?
Graphical Trend Analysis
Incremental Case Analysis
Incremental Complexity Analysis
Maximum Data Reduction
Are There Winners and Losers in Performance?
Getting the Best Results
Bibliographic and Historical Remarks
Art or Science? Case Studies in Data Mining
Why These Case Studies?
A Summary of Tasks for Predictive Data Mining
A Checklist for Data Preparation
A Checklist for Data Reduction
A Checklist for Data Modeling and Prediction
A Checklist for Case and Solution Analyses
The Case Studies
Transaction Processing
Text Mining
Outcomes Analysis
Process Control
Marketing and User Profiling
Exploratory Analysis
Looking Ahead
Bibliographic and Historical Remarks
Data-Miner Software Kit
References
Author Index
Subject Index