1. Internet Advertisements: This dataset represents a set of possible advertisements on Internet pages.
2. KDD Cup 1998 Data: This is the data set used for The Second International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-98
3. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
4. Insurance Company Benchmark (COIL 2000): This data set used in the CoIL 2000 Challenge contains information on customers of an insurance company. The data consists of 86 variables and includes product usage data and socio-demographic data
5. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
6. Internet Usage Data: This data contains general demographic information on internet users in 1997.
7. IPUMS Census Database: This data set contains unweighted PUMS census data from the Los Angeles and Long Beach areas for the years 1970, 1980, and 1990.
8. Covertype: Forest CoverType dataset
9. Sponge: Data on sponges; Attributes in spanish
10. 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
11. 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.
12. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing
13. Annealing: Steel annealing data
14. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.
15. Flags: From Collins Gem Guide to Flags, 1986
16. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values
17. Automobile: From 1985 Ward's Automotive Yearbook
18. Chess (King-Rook vs. King-Knight): Knight Pin Chess End-Game Database Creator
19. 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).
20. Thyroid Disease: 10 separate databases from Garavan Institute
21. Diabetes: This diabetes dataset is from AIM '94
22. 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
23. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)
24. University: Data in original (LISP-readable) form
25. Zoo: Artificial, 7 classes of animals
26. Coil 1999 Competition Data: This data set is from the 1999 Computational Intelligence and Learning (COIL) competition. The data contains measurements of river chemical concentrations and algae densities.
27. Labor Relations: From Collective Bargaining Review
28. Credit Approval: This data concerns credit card applications; good mix of attributes
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. Adult: Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
31. Census Income: Predict whether income exceeds $50K/yr based on census data. Also known as "Adult" dataset.
32. Housing: Taken from StatLib library
33. 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
34. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
35. 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
36. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack
37. Poker Hand: Purpose is to predict poker hands
38. Contraceptive Method Choice: Dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey.
39. Abalone: Predict the age of abalone from physical measurements
40. Auto MPG: Revised from CMU StatLib library, data concerns city-cycle fuel consumption
41. Mechanical Analysis: Fault diagnosis problem of electromechanical devices; also PUMPS DATA SET is newer version with domain theory and results
42. Post-Operative Patient: Dataset of patient features
43. Artificial Characters: Dataset artificially generated by using first order theory which describes structure of ten capital letters of English alphabet
44. Liver Disorders: BUPA Medical Research Ltd. database donated by Richard S. Forsyth
45. Chess (King-Rook vs. King): Chess Endgame Database for White King and Rook against Black King (KRK).
46. 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.
47. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
48. Servo: Data was from a simulation of a servo system
49. EEG Database: This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz
50. CalIt2 Building People Counts: This data comes from the main door of the CalIt2 building at UCI.
51. Dodgers Loop Sensor: Loop sensor data was collected for the Glendale on ramp for the 101 North freeway in Los Angeles
52. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
53. Pioneer-1 Mobile Robot Data: This dataset contains time series sensor readings of the Pioneer-1 mobile robot. The data is broken into "experiences" in which the robot takes action for some period of time and experiences a control
54. 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.