| |
Iris Plants (VER vs VIR) data
The data set (and description) can be downloaded here:
http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
Description:
1. Title: Iris Plants Database
Updated Sept 21 by C.Blake - Added discrepency information
2. Sources:
(a) Creator: R.A. Fisher
(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
(c) Date: July, 1988
3. Past Usage:
- Publications: too many to mention!!! Here are a few.
1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
to Mathematical Statistics" (John Wiley, NY, 1950).
2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
Structure and Classification Rule for Recognition in Partially Exposed
Environments". IEEE Transactions on Pattern Analysis and Machine
Intelligence, Vol. PAMI-2, No. 1, 67-71.
-- Results:
-- very low misclassification rates (0% for the setosa class)
4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
Transactions on Information Theory, May 1972, 431-433.
-- Results:
-- very low misclassification rates again
5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
conceptual clustering system finds 3 classes in the data.
4. Relevant Information:
--- This is perhaps the best known database to be found in the pattern
recognition literature. Fisher's paper is a classic in the field
and is referenced frequently to this day. (See Duda & Hart, for
example.) The data set contains 3 classes of 50 instances each,
where each class refers to a type of iris plant. One class is
linearly separable from the other 2; the latter are NOT linearly
separable from each other.
--- Predicted attribute: class of iris plant.
--- This is an exceedingly simple domain.
--- This data differs from the data presented in Fishers article
(identified by Steve Chadwick, spchadwick@espeedaz.net )
The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
where the error is in the fourth feature.
The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
where the errors are in the second and third features.
5. Number of Instances: 150 (50 in each of three classes)
6. Number of Attributes: 4 numeric, predictive attributes and the class
7. Attribute Information:
1. sepal length in cm
2. sepal width in cm
3. petal length in cm
4. petal width in cm
5. class:
-- Iris Setosa
-- Iris Versicolour
-- Iris Virginica
8. Missing Attribute Values: None
Summary Statistics:
Min Max Mean SD Class Correlation
sepal length: 4.3 7.9 5.84 0.83 0.7826
sepal width: 2.0 4.4 3.05 0.43 -0.4194
petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
9. Class Distribution: 33.3% for each of 3 classes.
Citation Request:
Please refer to the repository http://archive.ics.uci.edu/ml (see citation policy).
See also Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository
[http://archive.ics.uci.edu/ml].
Irvine, CA: University of California, School of Information and Computer Science.
Descriptive statistics:
Dataset= iris_versicolorvsvirginica : n= 100 , d= 4
Class1: n= 50
Covariance matrix:
[,1] [,2] [,3] [,4]
[1,] 0.2664 0.0852 0.1829 0.0558
[2,] 0.0852 0.0985 0.0827 0.0412
[3,] 0.1829 0.0827 0.2208 0.0731
[4,] 0.0558 0.0412 0.0731 0.0391
Correlation matrix:
[,1] [,2] [,3] [,4]
[1,] 1.0000 0.5259 0.7540 0.5465
[2,] 0.5259 1.0000 0.5605 0.6640
[3,] 0.7540 0.5605 1.0000 0.7867
[4,] 0.5465 0.6640 0.7867 1.0000
Median: 5.9113 2.7996 4.2731 1.3255
Mean: 5.936 2.77 4.26 1.326
MCD-estimated:
MDC-0.975-Mean: 5.9146 2.8098 4.2268 1.3073
MDC-0.750-Mean: 5.9205 2.8154 4.2026 1.3051
MDC-0.500-Mean: 5.9146 2.8098 4.2268 1.3073
Class2: n= 50
Covariance matrix:
[,1] [,2] [,3] [,4]
[1,] 0.4043 0.0938 0.3033 0.0491
[2,] 0.0938 0.1040 0.0714 0.0476
[3,] 0.3033 0.0714 0.3046 0.0488
[4,] 0.0491 0.0476 0.0488 0.0754
Correlation matrix:
[,1] [,2] [,3] [,4]
[1,] 1.0000 0.4572 0.8642 0.2811
[2,] 0.4572 1.0000 0.4010 0.5377
[3,] 0.8642 0.4010 1.0000 0.3221
[4,] 0.2811 0.5377 0.3221 1.0000
Median: 6.5421 2.9864 5.4953 2.0428
Mean: 6.588 2.974 5.552 2.026
MCD-estimated:
MDC-0.975-Mean: 6.4622 2.9489 5.4289 2.0156
MDC-0.750-Mean: 6.4622 2.9489 5.4289 2.0156
MDC-0.500-Mean: 6.4256 2.9488 5.4 2.0302
Measures:
Mah.Dist: 3.7708
Mah.Dist-MCD-0.975: 4.1579
Mah.Dist-MCD-0.750: 4.0015
Mah.Dist-MCD-0.500: 4.1055
All the MCD estimates have been obtained after a slight perturbation of the data set
Zuletzt geändert am 17.02.2013
|
|
|
