1. Fertility: 100 volunteers provide a semen sample analyzed according to the WHO 2010 criteria. Sperm concentration are related to socio-demographic data, environmental factors, health status, and life habits
2. BLOGGER: In this paper, we look for to recognize the causes of users tend
to cyber space in Kohkiloye and Boyer Ahmad Province in
3. Zoo: Artificial, 7 classes of animals
4. Breast Tissue: Dataset with electrical impedance measurements of freshly excised tissue samples from the breast.
5. Pittsburgh Bridges: Bridges database that has original and numeric-discretized datasets
6. Northix: Northix is designed to be a schema matching benchmark problem for data integration of two entity relationship databases.
7. 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.
8. Japanese Credit Screening: Includes domain theory (generated by talking to Japanese domain experts); data in Lisp
9. LSVT Voice Rehabilitation: 126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem).
10. Echocardiogram: Data for classifying if patients will survive for at least one year after a heart attack
11. Lymphography: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. (Restricted access)
12. Iris: Famous database; from Fisher, 1936
13. Teaching Assistant Evaluation: The data consist of evaluations of teaching performance; scores are "low", "medium", or "high"
14. Hepatitis: From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)
15. Hayes-Roth: Topic: human subjects study
16. Wine: Using chemical analysis determine the origin of wines
17. Flags: From Collins Gem Guide to Flags, 1986
18. Parkinsons: Oxford Parkinson's Disease Detection Dataset
19. Breast Cancer Wisconsin (Prognostic): Prognostic Wisconsin Breast Cancer Database
20. Connectionist Bench (Sonar, Mines vs. Rocks): The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
21. Mechanical Analysis: Fault diagnosis problem of electromechanical devices; also PUMPS DATA SET is newer version with domain theory and results
22. seeds: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes.
23. Glass Identification: From USA Forensic Science Service; 6 types of glass; defined in terms of their oxide content (i.e. Na, Fe, K, etc)
24. Audiology (Original): Nominal audiology dataset from Baylor
25. Audiology (Standardized): Standardized version of the original audiology database
26. Daphnet Freezing of Gait: This dataset contains the annotated readings of 3 acceleration sensors at the hip and leg of Parkinson's disease patients that experience freezing of gait (FoG) during walking tasks.
27. Qualitative_Bankruptcy: Predict the Bankruptcy from Qualitative parameters from experts.
28. SPECT Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
29. SPECTF Heart: Data on cardiac Single Proton Emission Computed Tomography (SPECT) images. Each patient classified into two categories: normal and abnormal.
30. 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
31. University: Data in original (LISP-readable) form
32. Breast Cancer: Breast Cancer Data (Restricted Access)
33. Heart Disease: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach
34. Haberman's Survival: Dataset contains cases from study conducted on the survival of patients who had undergone surgery for breast cancer
35. Soybean (Large): Michalski's famous soybean disease database
36. Vertebral Column: Data set containing values for six biomechanical features used to classify orthopaedic patients into 3 classes (normal, disk hernia or spondilolysthesis) or 2 classes (normal or abnormal).
37. Syskill and Webert Web Page Ratings: This database contains HTML source of web pages plus the ratings of a single user on these web pages. Web pages are on four seperate subjects (Bands- recording artists; Goats; Sheep; and BioMedical)
38. Ecoli: This data contains protein localization sites
39. Primary Tumor: From Ljubljana Oncology Institute
40. Leaf: This dataset consists in a collection of shape and texture features extracted from digital images of leaf specimens originating from a total of 40 different plant species.
41. Ionosphere: Classification of radar returns from the ionosphere
42. 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).
43. Dermatology: Aim for this dataset is to determine the type of Eryhemato-Squamous Disease.
44. Horse Colic: Well documented attributes; 368 instances with 28 attributes (continuous, discrete, and nominal); 30% missing values
45. User Knowledge Modeling: It is the real dataset about the students' knowledge status about the subject of Electrical DC Machines. The dataset had been obtained from Ph.D. Thesis.
46. ser Knowledge Modeling Data (Students' Knowledge Levels on DC Electrical Machines): The dataset is about the users' learning activities and knowledge levels on subjects of DC Electrical Machines. The dataset had been obtained from online web-courses and reported in my Ph.D. Thesis.
47. MONK's Problems: A set of three artificial domains over the same attribute space; Used to test a wide range of induction algorithms
48. Congressional Voting Records: 1984 United Stated Congressional Voting Records; Classify as Republican or Democrat
49. Wholesale customers: The data set refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories
50. 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.
51. Arrhythmia: Distinguish between the presence and absence of cardiac arrhythmia and classify it in one of the 16 groups.
52. Robot Execution Failures: This dataset contains force and torque measurements on a robot after failure detection. Each failure is characterized by 15 force/torque samples collected at regular time intervals
53. Thoracic Surgery Data: The data is dedicated to classification problem related to the post-operative life expectancy in the lung cancer patients: class 1 - death within one year after surgery, class 2 - survival.
54. Musk (Version 1): The goal is to learn to predict whether new molecules will be musks or non-musks
55. Demospongiae: Marine sponges of the Demospongiae class classification domain.
56. Cylinder Bands: Used in decision tree induction for mitigating process delays known as "cylinder bands" in rotogravure printing
57. 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).
58. Low Resolution Spectrometer: From IRAS data -- NASA Ames Research Center
59. ISTANBUL STOCK EXCHANGE: Data sets includes returns of Istanbul Stock Exchange with seven other international index; SP, DAX, FTSE, NIKKEI, BOVESPA, MSCE_EU, MSCI_EM from Jun 5, 2009 to Feb 22, 2011.
60. Climate Model Simulation Crashes: Given Latin hypercube samples of 18 climate model input parameter values, predict climate model simulation crashes and determine the parameter value combinations that cause the failures.
61. Breast Cancer Wisconsin (Diagnostic): Diagnostic Wisconsin Breast Cancer Database
62. ILPD (Indian Liver Patient Dataset): This data set contains 10 variables that are age, gender, total Bilirubin, direct Bilirubin, total proteins, albumin, A/G ratio, SGPT, SGOT and Alkphos.
63. Balance Scale: Balance scale weight & distance database
64. Japanese Vowels: This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers.
65. Credit Approval: This data concerns credit card applications; good mix of attributes
66. 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
67. Breast Cancer Wisconsin (Original): Original Wisconsin Breast Cancer Database
68. Blood Transfusion Service Center: Data taken from the Blood Transfusion Service Center in Hsin-Chu City in Taiwan -- this is a classification problem.
69. Pima Indians Diabetes: From National Institute of Diabetes and Digestive and Kidney Diseases; Includes cost data (donated by Peter Turney)
70. 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.
71. Annealing: Steel annealing data
72. Arcene: ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge.
73. MicroMass: A dataset to explore machine learning approaches for the identification of microorganisms from mass-spectrometry data.
74. Statlog (Vehicle Silhouettes): 3D objects within a 2D image by application of an ensemble of shape feature extractors to the 2D silhouettes of the objects.
75. Tic-Tac-Toe Endgame: Binary classification task on possible configurations of tic-tac-toe game
76. Mammographic Mass: Discrimination of benign and malignant mammographic masses based on BI-RADS attributes and the patient's age.
77. 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