60 lines
		
	
	
		
			2.6 KiB
		
	
	
	
		
			ReStructuredText
		
	
	
		
		
			
		
	
	
			60 lines
		
	
	
		
			2.6 KiB
		
	
	
	
		
			ReStructuredText
		
	
	
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								Iris Plants Database
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								Notes
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								-----
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								Data Set Characteristics:
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								    :Number of Instances: 150 (50 in each of three classes)
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								    :Number of Attributes: 4 numeric, predictive attributes and the class
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								    :Attribute Information:
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								        - sepal length in cm
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								        - sepal width in cm
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								        - petal length in cm
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								        - petal width in cm
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								        - class:
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								                - Iris-Setosa
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								                - Iris-Versicolour
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								                - Iris-Virginica
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								    :Summary Statistics:
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								    ============== ==== ==== ======= ===== ====================
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								                    Min  Max   Mean    SD   Class Correlation
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								    ============== ==== ==== ======= ===== ====================
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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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								    ============== ==== ==== ======= ===== ====================
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								    :Missing Attribute Values: None
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								    :Class Distribution: 33.3% for each of 3 classes.
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								    :Creator: R.A. Fisher
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								    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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								    :Date: July, 1988
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								This is a copy of UCI ML iris datasets.
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								http://archive.ics.uci.edu/ml/datasets/Iris
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								The famous Iris database, first used by Sir R.A Fisher
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								This is perhaps the best known database to be found in the
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								pattern recognition literature.  Fisher's paper is a classic in the field and
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								is referenced frequently to this day.  (See Duda & Hart, for example.)  The
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								data set contains 3 classes of 50 instances each, where each class refers to a
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								type of iris plant.  One class is linearly separable from the other 2; the
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								latter are NOT linearly separable from each other.
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								References
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								----------
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								   - 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 to
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								     Mathematical Statistics" (John Wiley, NY, 1950).
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								   - 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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								   - 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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								   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
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								     on Information Theory, May 1972, 431-433.
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								   - 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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								   - Many, many more ...
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