Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

Browse Through:

Default Task

Classification (25)
Regression (3)
Clustering (0)
Other (1)

Attribute Type

Categorical (5)
Numerical (13)
Mixed (7)

Data Type - Undo

Multivariate (26)
Univariate (0)
Sequential (1)
Time-Series (0)
Text (0)
Domain-Theory (1)
Other (0)

Area - Undo

Life Sciences (26)
Physical Sciences (11)
CS / Engineering (6)
Social Sciences (3)
Business (5)
Game (0)
Other (11)

# Attributes - Undo

Less than 10 (11)
10 to 100 (26)
Greater than 100 (5)

# Instances - Undo

Less than 100 (3)
100 to 1000 (26)
Greater than 1000 (14)

Format Type

Matrix (20)
Non-Matrix (6)

26 Data Sets

Table View  List View

1. Abscisic Acid Signaling Network: The objective is to determine the set of boolean rules that describe the interactions of the nodes within this plant signaling network. The dataset includes 300 separate boolean pseudodynamic simulations using an asynchronous update scheme.

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

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

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

5. Thoracic Surgery Data: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.

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

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

8. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach

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

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

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

12. Forest type mapping: Multi-temporal remote sensing data of a forested area in Japan. The goal is to map different forest types using spectral data.

13. Primary Tumor: From Ljubljana Oncology Institute

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

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

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

17. Zoo: Artificial, 7 classes of animals

18. Cervical cancer (Risk Factors): This dataset focuses on the prediction of indicators/diagnosis of cervical cancer. The features cover demographic information, habits, and historic medical records.

19. Quality Assessment of Digital Colposcopies: This dataset explores the subjective quality assessment of digital colposcopies.

20. 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

21. Parkinsons: Oxford Parkinson's Disease Detection Dataset

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

23. ILPD (Indian Liver Patient Dataset): This data set contains 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos.

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

25. Early biomarkers of Parkinson’s disease based on natural connected speech: Predict a pattern of neurodegeneration in the dataset of speech features obtained from patients with early untreated Parkinson’s disease and patients at high risk developing Parkinson’s disease.

26. Fertility: 100 volunteers provide a semen sample analyzed according to the WHO 2010 criteria. Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML