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 (13)
Regression (2)
Clustering (5)
Other (2)

Attribute Type - Undo

Categorical (5)
Numerical (17)
Mixed (0)

Data Type - Undo

Multivariate (132)
Univariate (12)
Sequential (17)
Time-Series (29)
Text (11)
Domain-Theory (6)
Other (8)

Area

Life Sciences (2)
Physical Sciences (1)
CS / Engineering (8)
Social Sciences (0)
Business (0)
Game (0)
Other (5)

# Attributes

Less than 10 (3)
10 to 100 (8)
Greater than 100 (1)

# Instances

Less than 100 (1)
100 to 1000 (2)
Greater than 1000 (12)

Format Type

Matrix (9)
Non-Matrix (8)

17 Data Sets

Table View  List View


1. UJI Pen Characters: Data consists of written characters in a UNIPEN-like format

2. Hill-Valley: Each record represents 100 points on a two-dimensional graph. When plotted in order (from 1 through 100) as the Y co-ordinate, the points will create either a Hill (a “bump” in the terrain) or a Valley (a “dip” in the terrain).

3. Ozone Level Detection: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area.

4. UJI Pen Characters (Version 2): A pen-based database with more than 11k isolated handwritten characters

5. Libras Movement: The data set contains 15 classes of 24 instances each. Each class references to a hand movement type in LIBRAS (Portuguese name 'LÍngua BRAsileira de Sinais', oficial brazilian signal language).

6. 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'.

7. Localization Data for Person Activity: Data contains recordings of five people performing different activities. Each person wore four sensors (tags) while performing the same scenario five times.

8. Online Handwritten Assamese Characters Dataset: This is a dataset of 8235 online handwritten assamese characters. The “online” process involves capturing of data as text is written on a digitizing tablet with an electronic pen.

9. QtyT40I10D100K: Since there is no numerical sequential data stream available in standard data sets, this data set is generated from the original T40I10D100K data set

10. Wearable Computing: Classification of Body Postures and Movements (PUC-Rio): A dataset with 5 classes (sitting-down, standing-up, standing, walking, and sitting) collected on 8 hours of activities of 4 healthy subjects. We also established a baseline performance index.

11. 3D Road Network (North Jutland, Denmark): 3D road network with highly accurate elevation information (+-20cm) from Denmark used in eco-routing and fuel/Co2-estimation routing algorithms.

12. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state.

13. Predict keywords activities in a online social media: The data from Twitter was collected during 360 consecutive days. It was done by querying 1497 English keywords sampled from Wikipedia. This dataset is proposed in a Learning to rank setting.

14. SML2010: This dataset is collected from a monitor system mounted in a domotic house. It corresponds to approximately 40 days of monitoring data.

15. User Identification From Walking Activity: The dataset collects data from an Android smartphone positioned in the chest pocket from 22 participants walking in the wild over a predefined path.

16. Activity Recognition from Single Chest-Mounted Accelerometer: The dataset collects data from a wearable accelerometer mounted on the chest. The dataset is intended for Activity Recognition research purposes.

17. Gesture Phase Segmentation: The dataset is composed by features extracted from 7 videos with people gesticulating, aiming at studying Gesture Phase Segmentation. It contains 50 attributes divided into two files for each video.


Supported By:

 In Collaboration With:

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