1. ISOLET: Goal: Predict which letter-name was spoken--a simple classification task.
2. SECOM: Data from a semi-conductor manufacturing process
3. Concrete Slump Test: Concrete is a highly complex material. The slump flow of concrete is not only determined by the water content, but that is also influenced by other concrete ingredients.
4. Wall-Following Robot Navigation Data: The data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'.
5. PEMS-SF: 15 months worth of daily data (440 daily records) that describes the occupancy rate, between 0 and 1, of different car lanes of the San Francisco bay area freeways across time.
6. Reuter_50_50: The dataset is used for authorship identification in online Writeprint which is a new research field of pattern recognition.
7. Gas Sensor Array Drift Dataset: This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations.
8. OPPORTUNITY Activity Recognition: The OPPORTUNITY Dataset for Human Activity Recognition from Wearable, Object, and Ambient Sensors is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc).
9. PAMAP2 Physical Activity Monitoring: The PAMAP2 Physical Activity Monitoring dataset contains data of 18 different physical activities, performed by 9 subjects wearing 3 inertial measurement units and a heart rate monitor.
10. Multiple Features: This dataset consists of features of handwritten numerals (`0'--`9') extracted from a collection of Dutch utility maps
11. Page Blocks Classification: The problem consists of classifying all the blocks of the page layout of a document that has been detected by a segmentation process.
12. Spambase: Classifying Email as Spam or Non-Spam
13. URL Reputation: Anonymized 120-day subset of the ICML-09 URL data containing 2.4 million examples and 3.2 million features.
14. Energy efficiency: This study looked into assessing the heating load and cooling load requirements of buildings (that is, energy efficiency) as a function of building parameters.
15. Computer Hardware: Relative CPU Performance Data, described in terms of its cycle time, memory size, etc.
16. Letter Recognition: Database of character image features; try to identify the letter
17. Optical Recognition of Handwritten Digits: Two versions of this database available; see folder
18. Pen-Based Recognition of Handwritten Digits: Digit database of 250 samples from 44 writers
19. Gisette: GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
20. Semeion Handwritten Digit: 1593 handwritten digits from around 80 persons were scanned, stretched in a rectangular box 16x16 in a gray scale of 256 values.
21. 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
22. Internet Advertisements: This dataset represents a set of possible advertisements on Internet pages.
23. Servo: Data was from a simulation of a servo system
24. Internet Usage Data: This data contains general demographic information on internet users in 1997.
25. 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
26. LED Display Domain: From Classification and Regression Trees book; We provide here 2 C programs for generating sample databases
27. Restaurant & consumer data: The dataset was obtained from a recommender system prototype. The task was to generate a top-n list of restaurants according to the consumer preferences.
28. Human Activity Recognition Using Smartphones: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.