1. Abalone: Predict the age of abalone from physical measurements
2. Adult: Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
3. Annealing: Steel annealing data
4. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
5. Artificial Characters: Dataset artificially generated by using first order theory which describes structure of ten capital letters of English alphabet
6. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
7. Census Income: Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset.
8. Chess (King-Rook vs. King-Knight): Knight Pin Chess End-Game Database Creator
9. Chess (King-Rook vs. King): Chess Endgame Database for White King and Rook against Black King (KRK).
10. Credit Approval: This data concerns credit card applications; good mix of attributes
11. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
12. Contraceptive Method Choice: Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.
13. Covertype: Forest CoverType dataset
14. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing
15. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.
16. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack
17. Flags: From Collins Gem Guide to Flags, 1986
18. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
19. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)
20. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values
21. Internet Advertisements: This dataset represents a set of possible advertisements on Internet pages.
22. Mechanical Analysis: Fault diagnosis problem of electromechanical devices; also PUMPS DATA SET is newer version with domain theory and results
23. 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).
24. Post-Operative Patient: Dataset of patient features
25. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
26. Thyroid Disease: 10 separate databases from Garavan Institute
27. University: Data in original (LISP-readable) form
28. Zoo: Artificial, 7 classes of animals
29. Australian Sign Language signs: This data consists of sample of Auslan (Australian Sign Language) signs. Examples of 95 signs were collected from five signers with a total of 6650 sign samples.
30. Census-Income (KDD): This data set contains weighted census data extracted from the 1994 and 1995 current population surveys conducted by the U.S. Census Bureau.
31. KDD Cup 1999 Data: This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99
32. 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
33. 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
34. 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
35. Poker Hand: Purpose is to predict poker hands
36. Acute Inflammations: The data was created by a medical expert as a data set to test the expert system,
which will perform the presumptive diagnosis of two diseases of the urinary system.
37. AutoUniv: AutoUniv is an advanced data generator for classifications tasks. The aim is to reflect the nuances and heterogeneity of real data. Data can be generated in .csv, ARFF or C4.5 formats.