ACADEMIA

A wide range of business, government, and academic research initiatives that had previously been limited by the effectiveness of commercially available data mining software can now build practical and highly functional data mining applications using the newly released version 2.0 of NAG Data Mining and Cleaning Components (DMC 2.0). DMC 2.0 is the first commercially available data mining application development toolkit that uses results from a three-year European Union funded project, EUREDIT, among other advances in data mining techniques developed by the Numerical Algorithms Group, a global collaborative network of 300+ computer scientists and mathematical experts that work together to solve complex mathematical problems. DMC 2.0 improvements over previous commercially available data mining applications include: 1) enhanced DATA CLEANING to resolve the problems of missing, invalid or incomplete data (data imputation methods); 2) advances in OUTLIER IDENTIFICATION to determine which datasets are suitable for analysis; 3) newly MEMORY-EFFICIENT MULTIVARIATE STATISTICAL METHODS that have been the traditional core of data mining techniques; and, 4) a wide range of added functionality for MACHINE LEARNING and PATTERN RECOGNITION. (For a complete list of DMC 2.0 functionality please see http://www.nag.com/numeric/DR/Drfunctionality.asp .) The European Union funded EUREDIT project in which many of the DMC 2.0 methods were first developed was geared to find new statistical methods important to various areas of government-sponsored socioeconomic studies. Mathematical experts associated with NAG further developed these algorithms and combined them with other computational functions for the breadth of data mining functionality including data cleaning, data transformations, outlier detection, clustering, classification, regression, association rules, and components for utility functions. A hyperlinked PDF User Guide directs users to detailed function documents for the problem they wish to solve. Written in ANSI C with simple function interfaces, NAG DMC 2.0 is ideally suited for interfacing with other programming languages such as PERL, Java, C#, and Python. NAG DMC 2.0 is currently available for Windows. Mac OS X, Linux, AIX (32-bit), Solaris, Alpha versions will be available later this month. Unlike most other commercially available data mining tools, DMC 2.0, like earlier releases of NAG Data Mining Components, is designed to be incorporated into the user’s application rather than requiring the user to learn a new interface to gain access to additional techniques. Users select the components they need for problem solving and can readily integrate these components into existing applications. NAG (www.nag.com) is a 30-year-old company dedicated to making cross-platform mathematical, statistical, data mining components and tools for developers as well as 3D visualization application development environments. It operates worldwide with hubs in Chicago (Downers Grove), UK (Oxford), and Tokyo. Today it serves over 10,000 sites worldwide in finance, engineering, and scientific research as well as commercial software firms such as PeopleSoft, IBM/Informix, Intel, and many others.