70 lines
2.9 KiB
Plaintext
70 lines
2.9 KiB
Plaintext
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1. Title: Iris Plants Database
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Updated Sept 21 by C.Blake - Added discrepency information
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2. Sources:
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(a) Creator: R.A. Fisher
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(b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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(c) Date: July, 1988
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3. Past Usage:
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- Publications: too many to mention!!! Here are a few.
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1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
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to Mathematical Statistics" (John Wiley, NY, 1950).
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2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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-- Results:
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-- very low misclassification rates (0% for the setosa class)
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4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE
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Transactions on Information Theory, May 1972, 431-433.
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-- Results:
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-- very low misclassification rates again
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5. See also: 1988 MLC Proceedings, 54-64. Cheeseman et al's AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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4. Relevant Information:
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--- This is perhaps the best known database to be found in the pattern
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recognition literature. Fisher's paper is a classic in the field
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and is referenced frequently to this day. (See Duda & Hart, for
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example.) The data set contains 3 classes of 50 instances each,
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where each class refers to a type of iris plant. One class is
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linearly separable from the other 2; the latter are NOT linearly
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separable from each other.
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--- Predicted attribute: class of iris plant.
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--- This is an exceedingly simple domain.
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--- This data differs from the data presented in Fishers article
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(identified by Steve Chadwick, spchadwick@espeedaz.net )
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The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa"
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where the error is in the fourth feature.
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The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa"
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where the errors are in the second and third features.
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5. Number of Instances: 150 (50 in each of three classes)
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6. Number of Attributes: 4 numeric, predictive attributes and the class
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7. Attribute Information:
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1. sepal length in cm
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2. sepal width in cm
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3. petal length in cm
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4. petal width in cm
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5. class:
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-- Iris Setosa
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-- Iris Versicolour
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-- Iris Virginica
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8. Missing Attribute Values: None
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Summary Statistics:
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Min Max Mean SD Class Correlation
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sepal length: 4.3 7.9 5.84 0.83 0.7826
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sepal width: 2.0 4.4 3.05 0.43 -0.4194
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petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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9. Class Distribution: 33.3% for each of 3 classes.
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