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35 Data Sets

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1. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.

2. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).

3. Annealing: Steel annealing data

4. Audiology (Standardized): Standardized version of the original audiology database

5. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database

6. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database

7. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database

8. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets

9. Credit Approval: This data concerns credit card applications; good mix of attributes

10. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing

11. Leaf: This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species.

12. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.

13. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack

14. Flags: From Collins Gem Guide to Flags, 1986

15. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)

16. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)

17. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values

18. Ionosphere: Classification of radar returns from the ionosphere

19. Lymphography: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access)

20. Meta-data: Meta-Data was used in order to give advice about which classification method is appropriate for a particular dataset (taken from results of Statlog project).

21. Primary Tumor: From Ljubljana Oncology Institute

22. Soybean (Large): Michalski's famous soybean disease database

23. SPECT Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.

24. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.

25. Congressional Voting Records: 1984 United Stated Congressional Voting Records; Classify as Republican or Democrat

26. Wine: Using chemical analysis determine the origin of wines

27. Zoo: Artificial, 7 classes of animals

28. Statlog (Australian Credit Approval): This file concerns credit card applications. This database exists elsewhere in the repository (Credit Screening Database) in a slightly different form

29. Statlog (German Credit Data): This dataset classifies people described by a set of attributes as good or bad credit risks. Comes in two formats (one all numeric). Also comes with a cost matrix

30. Statlog (Heart): This dataset is a heart disease database similar to a database already present in the repository (Heart Disease databases) but in a slightly different form

31. Statlog (Vehicle Silhouettes): 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects.

32. Connectionist Bench (Sonar, Mines vs. Rocks): The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.

33. Parkinsons: Oxford Parkinson's Disease Detection Dataset

34. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.

35. Climate Model Simulation Crashes: Given Latin hypercube samples of 18 climate model input parameter values, predict climate model simulation crashes and determine the parameter value combinations that cause the failures.


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