Abstract
In this paper we describe the open source data analytics platform KNIME, focusing particularly on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms, rule induction methods, and interactive clustering tools. In addition we outline a number of experimental extensions, which are not yet part of the open source release and present two illustrative examples from real world applications to demonstrate the power of the KNIME extensions.
Highlights
KNIME is a modular, opena platform for data integration, processing, analysis, and exploration 2
In this paper we describe the open source data analytics platform KNIME, focusing on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms, rule induction methods, and interactive clustering tools
In addition we outline a number of experimental extensions, which are not yet part of the open source release and present two illustrative examples from real world applications to demonstrate the power of the KNIME extensions
Summary
KNIME is a modular, opena platform for data integration, processing, analysis, and exploration 2. To the best of our knowledge none of the other popular open source data analysis or workflow environments[14,9,17] include fuzzy types and learning algorithms. One of the important design decisions was to ensure easy extensibility, so that other users can add functionality, usually in the form of new nodes (and sometimes data types). This has already been done by several commercial vendors (Tripos, Schrodinger, Chemical Computing Group, ...) and by other university groups and open source programmers. Before dicussing how fuzzy types and learning methods can be integrated into KNIME, let us first discuss the KNIME architecture in more detail
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