1. SUSY: This is a classification problem to distinguish between a signal process which produces supersymmetric particles and a background process which does not.
2. HIGGS: This is a classification problem to distinguish between a signal process which produces Higgs bosons and a background process which does not.
3. Phishing Websites: This dataset collected mainly from: PhishTank archive, MillerSmiles archive, Google’s searching operators.
4. seismic-bumps: The data describe the problem of high energy (higher than 10^4 J) seismic bumps forecasting in a coal
mine. Data come from two of longwalls located in a Polish coal mine.
5. Diabetes 130-US hospitals for years 1999-2008: This data has been prepared to analyze factors related to readmission as well as other
outcomes pertaining to patients with diabetes.
6. Parkinson Speech Dataset with Multiple Types of Sound Recordings: The training data belongs to 20 Parkinson's Disease (PD) patients and 20 healthy subjects. From all subjects, multiple types of sound recordings (26) are taken.
7. Image Segmentation: Image data described by high-level numeric-valued attributes, 7 classes
8. Letter Recognition: Database of character image features; try to identify the letter
9. Dataset for Sensorless Drive Diagnosis: Features are extracted from motor current. The motor has intact and defective components. This results in 11 different classes with different conditions.
10. Online News Popularity: This dataset summarizes a heterogeneous set of features about articles published by Mashable in a period of two years. The goal is to predict the number of shares in social networks (popularity).
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. Optical Recognition of Handwritten Digits: Two versions of this database available; see folder
13. Pen-Based Recognition of Handwritten Digits: Digit database of 250 samples from 44 writers
14. Mice Protein Expression: Expression levels of 77 proteins measured in the cerebral cortex of 8 classes of control and Down syndrome mice exposed to context fear conditioning, a task used to assess associative learning.
15. HEPMASS: The search for exotic particles requires sorting through a large number of collisions to find the events of interest. This data set challenges one to detect a new particle of unknown mass.
16. Spambase: Classifying Email as Spam or Non-Spam
17. default of credit card clients: This research aimed at the case of customers’ default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods.
18. STUDENT ALCOHOL CONSUMPTION: The result also provides the correlation between alcohol usage and the social, gender and study time attributes for each student.
19. Polish companies bankruptcy data: The dataset is about bankruptcy prediction of Polish companies.The bankrupt companies were analyzed in the period 2000-2012, while the still operating companies were evaluated from 2007 to 2013.
20. Statlog (Landsat Satellite): Multi-spectral values of pixels in 3x3 neighbourhoods in a satellite image, and the classification associated with the central pixel in each neighbourhood
21. Statlog (Image Segmentation): This dataset is an image segmentation database similar to a database already present in the repository (Image segmentation database) but in a slightly different form.
22. MAGIC Gamma Telescope: Data are MC generated to simulate registration of high energy gamma particles in an atmospheric Cherenkov telescope
23. Wine Quality: Two datasets are included, related to red and white vinho verde wine samples, from the north of Portugal. The goal is to model wine quality based on physicochemical tests (see [Cortez et al., 2009], http://www3.dsi.uminho.pt/pcortez/wine/).
24. Cardiotocography: The dataset consists of measurements of fetal heart rate (FHR) and uterine contraction (UC) features on cardiotocograms classified by expert obstetricians.
25. MiniBooNE particle identification: This dataset is taken from the MiniBooNE experiment and is used to distinguish electron neutrinos (signal) from muon neutrinos (background).
26. Record Linkage Comparison Patterns: Element-wise comparison of records with personal data from a record linkage setting. The task is to decide from a comparison pattern whether the underlying records belong to one person.
27. Bank Marketing: The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
28. First-order theorem proving: Given a theorem, predict which of five heuristics will give the fastest proof when used by a first-order prover. A sixth prediction declines to attempt a proof, should the theorem be too difficult.
29. QSAR biodegradation: Data set containing values for 41 attributes (molecular descriptors) used to classify 1055 chemicals into 2 classes (ready and not ready biodegradable).
30. Waveform Database Generator (Version 1): CART book's waveform domains
31. Waveform Database Generator (Version 2): CART book's waveform domains
32. Grammatical Facial Expressions: This dataset supports the development of models that make possible to interpret Grammatical Facial Expressions from Brazilian Sign Language (Libras).
33. 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'.
34. EEG Eye State: The data set consists of 14 EEG values and a value indicating the eye state.
35. 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.
36. UJIIndoorLoc-Mag: The UJIIndoorLoc-Mag is an indoor localization database to test Indoor Positioning System that rely on Earth's magnetic field variations.
37. Educational Process Mining (EPM): A Learning Analytics Data Set: Educational Process Mining data set is built from the recordings of 115 subjects' activities through a logging application while learning with an educational simulator.
38. 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.
39. Heterogeneity Activity Recognition: The Heterogeneity Human Activity Recognition (HHAR) dataset from Smartphones and Smartwatches is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc.) in real-world contexts; specifically, the dataset is gathered with a variety of different device models and use-scenarios, in order to reflect sensing heterogeneities to be expected in real deployments.
40. Gas sensors for home activity monitoring: 100 recordings of a sensor array under different conditions in a home setting: background, wine and banana presentations. The array includes 8 MOX gas sensors, and humidity and temperature sensors.
41. Australian Sign Language signs (High Quality): This data consists of sample of Auslan (Australian Sign Language) signs. 27 examples of each of 95 Auslan signs were captured from a native signer using high-quality position trackers
42. 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.
43. KEGG Metabolic Relation Network (Directed): KEGG Metabolic pathways modeled as directed relation network. Variety of graphical features presented.
44. KEGG Metabolic Reaction Network (Undirected): KEGG Metabolic pathways modeled as un-directed reaction network. Variety of graphical features presented.
45. 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.
46. Vicon Physical Action Data Set: The Physical Action Data Set includes 10 normal and 10 aggressive physical actions that measure the human activity. The data have been collected by 10 subjects using the Vicon 3D tracker.
47. Buzz in social media : This data-set contains examples of buzz events from two different social networks: Twitter, and Tom's Hardware, a forum network focusing on new technology with more conservative dynamics.