Advanced Data Mining Technologies in Bioinformatics

• Publisher Idea Group Publishing


• Author(s) Hui-Huang Hsu


• Release Date 25 May 2006

The technologies in data mining have been successfully applied to bioinformatics research in the past few years, but more research in this field is necessary. While tremendous progress has been made over the years, many of the fundamental challenges in bioinformatics are still open. Data mining plays an essential role in understanding the emerging problems in genomics, proteomics, and systems biology.

Advanced Data Mining Technologies in Bioinformatics covers important research topics of data mining on bioinformatics. Readers of this book will gain an understanding of the basics and problems of bioinformatics, as well as the applications of data mining technologies in tackling the problems and the essential research topics in the field. Advanced Data Mining Technologies in Bioinformatics is extremely useful for data mining researchers, molecular biologists, graduate students, and others interested in this topic.

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Data Mining Using SAS Enterprise Miner

• Publisher Wiley


• Author(s) Randall Matignon


• Release Date 03 August 2007

The most thorough and up-to-date introduction to data mining techniques using SAS Enterprise Miner.

The Sample, Explore, Modify, Model, and Assess (SEMMA) methodology of SAS Enterprise Miner is an extremely valuable analytical tool for making critical business and marketing decisions. Until now, there has been no single, authoritative book that explores every node relationship and pattern that is a part of the Enterprise Miner software with regard to SEMMA design and data mining analysis.

Data Mining Using SAS Enterprise Miner introduces readers to a wide variety of data mining techniques and explains the purpose of-and reasoning behind-every node that is a part of the Enterprise Miner software. Each chapter begins with a short introduction to the assortment of statistics that is generated from the various nodes in SAS Enterprise Miner v4.3, followed by detailed explanations of configuration settings that are located within each node.

Features of the book include:

The exploration of node relationships and patterns using data from an assortment of computations, charts, and graphs commonly used in SAS procedures

A step-by-step approach to each node discussion, along with an assortment of illustrations that acquaint the reader with the SAS Enterprise Miner working environment

Descriptive detail of the powerful Score node and associated SAS code, which showcases the important of managing, editing, executing, and creating custom-designed Score code for the benefit of fair and comprehensive business decision-making

Complete coverage of the wide variety of statistical techniques that can be performed using the SEMMA nodes

An accompanying Web site that provides downloadable Score code, training code, and data sets for further implementation, manipulation, and interpretation as well as SAS/IML software programming code

This book is a well-crafted study guide on the various methods employed to randomly sample, partition, graph, transform, filter, impute, replace, cluster, and process data as well as interactively group and iteratively process data while performing a wide variety of modeling techniques within the process flow of the SAS Enterprise Miner software. Data Mining Using SAS Enterprise Miner is suitable as a supplemental text for advanced undergraduate and graduate students of statistics and computer science and is also an invaluable, all-encompassing guide to data mining for novice statisticians and experts alike.

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Advanced Data Mining Techniques

• Publisher Springer-Verlag


• Author(s) Dursun Delen


• Release Date 01 February 2008

This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a number of different techniques often used in data mining. Part III focusses on business applications of data mining. Methods are presented with simple examples, applications are reviewed, and relativ advantages are evaluated.

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