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Iris Plants Database

Notes

Data Set Characteristics:
Number of Instances:

150 (50 in each of three classes)

Number of Attributes:

4 numeric, predictive attributes and the class

Attribute Information:
  • sepal length in cm
  • sepal width in cm
  • petal length in cm
  • petal width in cm
  • class:
    • Iris-Setosa
    • Iris-Versicolour
    • Iris-Virginica
Summary Statistics:
           
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!)
Missing Attribute Values:None
Class Distribution:33.3% for each of 3 classes.
Creator:R.A. Fisher
Donor:Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
Date:July, 1988

This is a copy of UCI ML iris datasets. http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

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.

References

  • 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).
  • 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.
  • 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.
  • Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433.
  • See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II conceptual clustering system finds 3 classes in the data.
  • Many, many more ...