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diff --git a/data/iris/iris.names b/data/iris/iris.names new file mode 100644 index 0000000..062b486 --- /dev/null +++ b/data/iris/iris.names @@ -0,0 +1,69 @@ +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. |