C4.5: programs for machine learning
C4.5: programs for machine learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Data preparation for data mining
Data preparation for data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Machine Learning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management
Computers and Electronics in Agriculture
Proceedings of the 2010 Conference of the Center for Advanced Studies on Collaborative Research
Improving classifier performance by knowledge-driven data preparation
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
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Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little "how to" information? And are you, like most analysts, preparing the data in SAS?This book is intended to fill this gap as your source of practical recipes. It introduces a framework for the process of data preparation for data mining, and presents the detailed implementation of each step in SAS. In addition, business applications of data mining modeling require you to deal with a large number of variables, typically hundreds if not thousands. Therefore, the book devotes several chapters to the methods of data transformation and variable selection.FEATURES * A complete framework for the data preparation process, including implementation details for each step. * The complete SAS implementation code, which is readily usable by professional analysts and data miners.* A unique and comprehensive approach for the treatment of missing values, optimal binning, and cardinality reduction.* Assumes minimal proficiency in SAS and includes a quick-start chapter on writing SAS macros.* CD includes dozens of SAS macros plus the sample data and the program for the book's case study.