	A	B
1	Name	Description
2	(generateData_<Name>.m files)
3	3D	Generates points scattered randomly around a linear 2D subspace of 3D
4	3DQuad	Generates noisy samples from a quadratic surface
5	4grid	Generates noisy labelled samples from 4 classes that are separable into 4 quadrants
6	5grid	Generates labelled samples from 5 classes that are separable into 5 regions of a grid
7	binary	Generates samples from two classes with a horizontal boundary
8	circular	Points in one class surround the other class
9	clustering	Generates samples from a randomly-chosen mixture of bivariate Gaussians
10	clustersXonly	Generates 2D sample of 5 tightly clustered groups of points on the plane, but no target labels
11	consGroups	Two distinct groups of constant-mean points centered at -0.5 and 0.5 respectively, separated by a discontinuity at 0
12	constant	Points are a noisy sample from a straight line centered at the origin
13	curved	Points are in each class are separable with a curved boundary
14	Gauss	Points follow noisy univariate Gaussian distribution
15	gridMulti	Generates noisy sample from 5 classes that live in 5 separate regions of the graph, but with substantial overlap, with target labels
16	groups	Points in each class are generated from orthogonal lines, forming an X with much overlap due to added noise
17	irrelevFeatures	Generate 10-D model matrix where most features are highly correlated but uninformative
18	linear	Points are a noise sample from a line with constant slope
19	linGroups	Points generated from -abs(x) under noise over [-1,1]
20	multiLabel	Generates 2D points with multiple labels represented as decimal numbers for binary label memberships. E.g., if 5 classes exists, and point x_1 belongs to classes 1 and 3 , its label is the binary number 00101 which is stored as the decimal number 5. Note: multilabel MLP function converts decimal representation back to binary representation for input to the network.
21	multiXonly	Generates labelled dataset with two clusters centered at (3,3) and (-3,-3) respectively
22	ordinal_1D	Generates 2D where class labels have an order imposed along along a line
23	ordinal_2D	Generates a 2D set of points with labels that have an order imposed according to a radial distance of the points from a center in the plane
24	outliersXonly	Generates samples from a bivariate standard normal distribution, with a few outliers samples from a bivariate normal with mean (5,5)
25	ovalXonly	Generates a a 2D sample from an oval region centered at the origin
26	quad	Points are a noisy sample from a quadratic function of inputs
27	rare	Generates data of one majority class with a relatively rare number of points of a second class
28	robustness	Generates points that are nearly linearly separable except for a few far outliers
29	sigmoid	Points follow noisy sigmoid shape
30	slanted	Points in each class are generated on either side of a linear boundary, with some overlap due to added noise
31	spreadOut	90% points are scattered around a line with constant slope, and the remaining 10% are scattered widely away from the line with constant slope
32	swissRoll	Generates a 3D dataset that resembles a plane curling inwards, intendended for manifold learning
33	vert	Points in each class are generate as two groups, stacked on top of each other with some overlap due to added noise
34	volatile	Generates a noisy sample from a stepwise function of sigmoid, Gaussian, and quadratic components
35	(<Name>.dat files)
36	animals	Contains classification features for various animals, intended for clustering
37	cities	Contains quality of life ratings for major U.S. metropolitan areas
38	data_exponential	Contains 2D poitns of two classes that are nearly linearly separable in a standard linear basis
39	outliersData	Contains 2D data exhibiting a strongly linear relationship but with numerous outliers
40	regressOnOne	Contains 2D data with constant variance across the range of x exhibiting a linear relationship between the dependent and independent variables