1. 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.
2. Bach Chorales: Time-series data based on chorales; challenge is to learn generative grammar; data in Lisp
3. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
4. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
5. Flags: From Collins Gem Guide to Flags, 1986
6. Automobile: From 1985 Ward's Automotive Yearbook
7. University: Data in original (LISP-readable) form
8. Auto MPG: Revised from CMU StatLib library, data concerns city-cycle fuel consumption
9. 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).
10. 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.
11. CalIt2 Building People Counts: This data comes from the main door of the CalIt2 building at UCI.
12. Dodgers Loop Sensor: Loop sensor data was collected for the Glendale on ramp for the 101 North freeway in Los Angeles
13. 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