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KNIME Desktop for Mac OS X

  • KNIME Desktop for Mac OS X
  • Note: this is still highly experimental. KNIME 2.1 is based on Eclipse 3.4.2, whereas for a functioning KNIME Max OS X integration we needed to use Eclipse 3.5. Hence the update site is not working due to known problems and incompatibilities with p2. Therefore this version includes all KNIME plugins. If you encounter any bugs, please let us know. Note, however, that we will likely not be able to seriously support this version before we switch over to Eclipse 3.5.
  • Requirements
  • Intel architecture
  • 64 bit
  • Java 1.6: Note that KNIME usually comes with a matching JRE. The Mac OS X version relies on an existing, compatible version of Java. If you encounter problems, try upgrading your java installation.
KNIME, pronounced [naim], is a modular data exploration platform that enables the user to visually create data flows (often referred to as pipelines), selectively execute some or all analysis steps, and later investigate the results through interactive views on data and models.
KNIME was developed (and will continue to be expanded) by the Chair for Bioinformatics and Information Mining at the University of Konstanz, Germany. The group headed by Michael Berthold also uses KNIME for teaching and research at the University. Quite a number of new data analysis methods developed at the chair are integrated in KNIME. Let us know if you are looking for something in particular, not all of those modules are part of the standard KNIME release just yet...
The KNIME base version already incorporates over 100 processing nodes for data I/O, preprocessing and cleansing, modeling, analysis and data mining as well as various interactive views, such as scatter plots, parallel coordinates and others. It integrates all analysis modules of the well known Weka data mining environment and additional plugins allow R-scripts to be run, offering access to a vast library of statistical routines.
KNIME is based on the Eclipse platform and, through its modular API, easily extensible. When desired, custom nodes and types can be implemented in KNIME within hours thus extending KNIME to comprehend and provide first-tier support for highly domain-specific data.
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