Predictive Data Mining: A Practical Guide
The potential business advantages of data mining are well documented in publications for executives and managers. However, developers implementing major data-mining systems need concrete information about the underlying technical principles-and their practical manifestations-in order to either integrate commercially available tools or write data-mining programs from scratch. This book is the first technical guide to provide a complete, generalized roadmap for developing data-mining applications, together with advice on performing these large-scale, open-ended analyses for real-world data warehouses.
Note: If you already own Predictive Data Mining: A Practical Guide, please see ISBN 1-55860-477-4 to order the accompanying software. To order the book/software package, please see ISBN 1-55860-478-2.
+ Focuses on the preparation and organization of data and the development of an overall strategy for data mining.
+ Reviews sophisticated prediction methods that search for patterns in big data.
+ Describes how to accurately estimate future performance of proposed solutions.
+ Illustrates the data-mining process and its potential pitfalls through real-life case studies.
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answer applications approach baseline big data bins Chapter classification complex solutions computational concepts Data Modeling data preparation data reduction data warehouse data-mining database decision tree decision-tree default deleted described Dimension Reduction distance effective entropy Equation error and complexity error rates evaluation example feature selection feature space feature values Figure goal hidden units increase induced large numbers logic methods math solutions mean measured missing values moving average neural nets node Noise Figure number of features number of values optimal parameters potential prediction methods prediction programs predictive data mining predictive performance principal components problems pruning real-world reducing the number regression Relative Error significance test single smoothing Solution Complexity specific standard error standard form standard spreadsheet form statistical strategy subsample subset summarizing techniques test data test error test set Text Mining Time-dependencies tion train and test training data transformations trends true-or-false types usually value reduction variable variance weights