Public Archive Data


  • Summary: (No information yet)
    Data Shape: 36 attributes, 683 instances (String)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (1.0 MB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: (No information yet)
    Data Shape: 17 attributes, 435 instances (String)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (313.6 KB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: (No information yet)
    Data Shape: 39 attributes, 898 instances (Integer,String)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (1.2 MB) XML CSV ARFF LibSVM Matlab Octave
    Files are converted on demand and the process can take up to a minute. Please wait until download begins.

  • Summary: (No information yet)
    Data Shape: 12 attributes, 576 instances (Integer,Floating Point)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (42.8 KB) XML CSV ARFF LibSVM Matlab Octave
    Files are converted on demand and the process can take up to a minute. Please wait until download begins.

  • Summary: (No information yet)
    Data Shape: 16 attributes, 159 instances (Integer,Floating Point)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (31.1 KB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: Biopsy samples of diffuse large-B-cell lymphoma from 240 patients were examined for gene expression with the use of DNA mocoarrays and analyzed for genomic abnormalities.
    License: unknown (from UCI repository)
    Tags: dlbcl NIH
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (14.1 MB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: (No information yet)
    Data Shape: 24482 attributes, 97 instances (Integer,Floating Point,String)
    License: unknown (from LibSVMTools repository)
    Tasks / Methods / Challenges: 1 tasks, 0 methods, 0 challenges
    Download: HDF5 (20.3 MB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: The diabetes data set from the IDA Benchmark repository.
    Data Shape: 9 attributes, 768 instances ()
    License: PDDL
    Tags: IDA_Benchmark_Repository
    Tasks / Methods / Challenges: 1 tasks, 0 methods, 0 challenges
    Download: HDF5 (64.5 KB) XML CSV ARFF LibSVM Matlab Octave
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  • Summary: Artificial data generated from the Friedman function, part of a collection of 80 data sets. This particular set has 10 features, 1 output (11th feature), colinearity degree 0, and 500 instances.
    Data Shape: 11 attributes, 500 instances (Floating Point)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (53.5 KB) XML CSV ARFF LibSVM Matlab Octave
    Files are converted on demand and the process can take up to a minute. Please wait until download begins.

  • Summary: Artificial data generated from the Friedman function, part of a collection of 80 data sets. This particular set has 50 features, 1 output (51st feature), colinearity degree 2, and 1000 instances.
    Data Shape: 51 attributes, 1000 instances (Floating Point)
    License: unknown (from Weka repository)
    Tags: arff slurped Weka
    Tasks / Methods / Challenges: 0 tasks, 0 methods, 0 challenges
    Download: HDF5 (409.0 KB) XML CSV ARFF LibSVM Matlab Octave
    Files are converted on demand and the process can take up to a minute. Please wait until download begins.

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Disclaimer

We are acting in good faith to make datasets submitted for the use of the scientific community available to everybody, but if you are a copyright holder and would like us to remove a dataset please inform us and we will do it as soon as possible.

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Acknowledgements

This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
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http://www.pascal-network.org/.