Data Mining using MLC++: A Machine Learning Library in C++

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Data Mining using MLC++: A Machine Learning Library in C++ (1997)

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by Ron Kohavi , Dan Sommerfield , James Dougherty

Venue:	INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Citations:	171 - 20 self

BibTeX

@ARTICLE{Kohavi97datamining,
    author = {Ron Kohavi and Dan Sommerfield and James Dougherty},
    title = {Data Mining using MLC++: A Machine Learning Library in C++},
    journal = {INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS},
    year = {1997},
    volume = {6},
    number = {4}
}

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Abstract

Data mining algorithmsincluding machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC++ , which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of different algorithms on a specific dataset of interest. MLC ++ not only provides a workbench for such comparisons, but also provides a library of C ++ classes to aid in the development of new algorithms, especially hybrid algorithms and multi-strategy algorithms. Such algorithms are generally hard to code from scratch. We discuss design issues, interfaces to other programs, and visualization of the resulting classifiers. 1 Introduction Data warehouses containing massive amounts of data have been b...

Keyphrases

data mining machine learning library classification algorithm pattern recognition technique specific dataset introduction data warehouse data warehouse multiple classification algorithm design issue machine learning appropriate classification algorithm different algorithm multi-strategy algorithm statistical analysis new algorithm massive amount hybrid algorithm

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