Delete iris.names

iris.names

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-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.