KNIME: The Konstanz Information Miner

By Michael R. Berthold, N. Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kötter, T. Meinl, Peter Ohl, C. Sieb, Kilian Thiel, Bernd Wiswedel. 2007

1,504 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

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Citations in the corpus

OpenML: networked science in machine learning (2014, methodology): “Moreover, OpenML links to data available anywhere online, and is being integrated [41] in popular data mining platforms such as Weka [13], R [5; 40], MOA [4], RapidMiner [42] and KNIME [3].”
Software tools for automated Transmission Electron Microscopy (2018, methodology): “This generic data-analysis software 21 includes image-processing capabilities 22 but also can incorporate modules from other platforms (e.g., ImageJ 23,24 , CellProfiler 25,26 , Ilastik 5 , R 27 , MATLAB) into a single workflow.”
A review of microarray datasets and applied feature selection methods (2014, methodology): “Allofthemareavailableinthewell-known Wekatool[53],exceptforthemRMRfilter,whoseimplementationisavailableforMatlab Ò.Althoughthereareotheracces- siblefeatureselectionmethodsimplementedinpopulartoolssuchasKMINE[19],KEEL[12]orFeatureSelectionAlgorithms fromArizonaStateUniversity[4],wehaveoptedforthesese”
ChemMine tools: an online service for analyzing and clustering small molecules (2011, methodology): “Examples of software designed for non-expert users in this field are Chembench (33) for online quantitative structure—activity relationship (QSAR) modeling and KNIME (34) for designing data analysis pipelines.”
KNIME - the Konstanz information miner: version 2.0 and beyond (2009, methodology): “Figure 1 shows a screenshot of the standard KNIME workbench with a small example data analysis work.ow.”
Big Data: A Survey (2014, background): “8 %): KNIME (Konstanz Information Miner) is a user-friendly, intelligent, and open-sourcerich data integration, data processing, data analysis, and data mining platform [113].”
A Structural Framework for GPCR Chemogenomics: What's In a Residue Number? (2018, methodology): “analytics platform [70] that exploits and integrates heterogeneous data sources for the prediction of GPCR-”
EHRs Connect Research and Practice: Where Predictive Modeling, Artificial Intelligence, and Clinical Decision Support Intersect (2012, methodology): “After the initial data loading and calculations were made, data was loaded into KNIME (Version 2.1.1) [22], an [20,23].”
Modelling Multi-Component Predictive Systems as Petri Nets (2017, methodology): “2016), RapidMiner (Hofmann and Klinkenberg 2016) or Knime (Berthold et al. 2008) allow to create and run MCPSs including a large variety of operators.”
Improving Data Quality through Deep Learning and Statistical Models (2018, methodology): “"The WEKA data mining software: an update."”