YALE will be presented at the twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006) in a paper and a poster presentation. The conference will take place between August, 20 - 23 in Philadelphia, USA. Read here about the contents of this paper and how to cite it.

Abstract

KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring the development of new methods or processing schemes. Like in software development, the development of such solutions demands careful analysis, specification, implementation, and testing. Rapid prototyping is an approach which allows crucial design decisions as early as possible. A rapid prototyping system should support maximal re-use and innovative combinations of existing methods, as well as simple and quick integration of new ones.

This paper describes Yale, a free open-source environment for KDD and machine learning. Yale provides a rich variety of methods which allows rapid prototyping for new applications and makes costly re-implementations unnecessary. Additionally, Yale offers extensive functionality for process evaluation and optimization which is a crucial property for any KDD rapid prototyping tool. Following the paradigm of visual programming eases the design of processing schemes. While the graphical user interface supports interactive design, the underlying XML representation enables automated applications after the prototyping phase.

After a discussion of the key concepts of Yale, the paper illustrates the advantages of rapid prototyping for KDD on case studies ranging from data pre-processing to result visualization. These case studies cover tasks like feature engineering, text mining, data stream mining and tracking drifting concepts, ensemble methods and distributed data mining. This variety of applications is also reflected in a broad user base, we counted more than 40,000 downloads during the last twelve months.

Download and Citation

You can click on the PDF symbol below in order to download the paper. If you use YALE in your scientific work, please cite this:

Mierswa, Ingo and Wurst, Michael and Klinkenberg, Ralf and Scholz, Martin and Euler, Timm: YALE: Rapid Prototyping for Complex Data Mining Tasks, in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06), 2006.

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