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.. _iris_dataset: |
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Iris plants dataset |
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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%[email protected]) |
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:Date: July, 1988 |
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The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken |
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from Fisher's paper. Note that it's the same as in R, but not as in the UCI |
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Machine Learning Repository, which has two wrong data points. |
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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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.. dropdown:: References |
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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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