Summary

(No information yet)

License

unknown (from Weka repository)

Dependencies

Tags

arff slurped Weka

Attribute Types

Integer,Floating Point

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# Instances: 3848 / # Attributes: 6
HDF5 (178.6 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

pollen

Version mldata

0

Comment

This dataset is synthetic. It was generated by David Coleman at RCA Laboratories in Princeton, N.J. For convenience, we will refer to it as the POLLEN DATA. The first three variables are the lengths of geometric features observed sampled pollen grains - in the x, y, and z dimensions: a "ridge" along x, a "nub" in the y direction, and a "crack" in along the z dimension. The fourth variable is pollen grain weight, and the fifth is density.

There are 3848 observations, in random order (for people whose software packages cannot handle this much data, it is recommended that the data be sampled). The dataset is broken up into eight pieces, POLLEN1.DAT - POLLEN8.DAT, each with 481 observations.
We will call the variables:

1. RIDGE

2. NUB

3. CRACK

4. WEIGHT

5. DENSITY

6. OBSERVATION NUMBER (for convenience)

The data analyst is advised that there is more than one "feature" to these data. Each feature can be observed through various graphical techniques, but analytic methods, as well, can help "crack" the dataset.

Additional Info:

I no longer have the description handed out during the JSM, but can tell you how I generated the data, in minitab.

1. Part A was generated: 5000 (I think) 5-variable, uncorrelated, i.i.d. Gaussian observations.

2. To get part B, I duplicated part A, then reversed the sign on the observations for 3 of the 5 variables.

3. Part B was appended to Part A.

4. The order of the observations was randomized.

5. While waiting for my tardy car-pool companion, I took a piece of graph paper, and figured out a dot-matrix representation of the word, "EUREKA." I then added these observations to the "center" of the datatset.

6. The data were scaled, by variable (something like 1,3,5,7,11).

7. The data were rotated, then translated.

8. A few points in space within the datacloud were chosen as ellipsoid centers, then for each center, all observations within a (scaled and rotated) radius were identified, and eliminated - to form ellipsoidal voids.

9. The variables were given entirely ficticious names.

FYI, only the folks at Bell Labs, Murray Hill, found everything, including the voids.

Hope this is helpful!

References:

Becker, R.A., Denby, L., McGill, R., and Wilks, A. (1986). Datacryptanalysis: A Case Study. Proceedings of the Section on Statistical Graphics, 92-97.

Slomka, M. (1986). The Analysis of a Synthetic Data Set. Proceedings of the Section on Statistical Graphics, 113-116.

Information about the dataset CLASSTYPE: numeric CLASSINDEX: none specific

Names

RIDGE,NUB,CRACK,WEIGHT,DENSITY,OBSERVATION_NUMBER,

Types

1. numeric
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric

Data (first 10 data points)

RIDGE	NUB	CRACK	WEIGHT	DENS...	OBSE...
-2.34...	3.6314	5.0289	10.8...	-1.38...	1.0
-1.152	1.4805	3.2375	-0.59...	2.1235	2.0
-2.52...	-6.86...	-2.80...	8.4631	-3.41...	3.0
5.7523	-6.50...	-5.151	4.348	-10.3...	4.0
8.7494	-3.89...	-1.38...	-14.8...	-2.41...	5.0
10.4...	-3.16...	12.7...	-14.8...	-6.49...	6.0
-3.60...	4.6081	6.554	5.9773	4.0404	7.0
-5.63...	-0.81...	-3.812	1.1674	7.0468	8.0
9.5434	4.0865	2.7542	-18.9...	-0.06...	9.0
-9.02...	2.9723	3.6759	13.882	4.2106	10.0
...	...	...	...	...	...

Description

A gzip'ed tar containing StatLib datasets (statlib-20050214.tar.gz, 12,785,582 Bytes)

URLs

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Publications

Data Source

http://lib.stat.cmu.edu/datasets/

Measurement Details

Usage Scenario

revision 1

by mldata on 2010-11-06 10:00

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