Data Set Characteristics: |
Multivariate |
Number of Instances: |
150 |
Area: |
Life |
Attribute Characteristics: |
Real |
Number of Attributes: |
4 |
Date Donated |
1988-07-01 |
Associated Tasks: |
Classification |
Missing Values? |
No |
Number of Web Hits: |
5313672 |
Source:
Creator:
R.A. Fisher
Donor:
Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov)
Data Set 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.
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
Relevant Papers:
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).
[Web Link]
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.
[Web Link]
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.
[Web Link]
Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433.
[Web Link]
See also: 1988 MLC Proceedings, 54-64.
Papers That Cite This Data Set1:
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Manuel Oliveira. Library Release Form Name of Author: Stanley Robson de Medeiros Oliveira Title of Thesis: Data Transformation For Privacy-Preserving Data Mining Degree: Doctor of Philosophy Year this Degree Granted. University of Alberta Library. 2005. [View Context].
Ping Zhong and Masao Fukushima. A Regularized Nonsmooth Newton Method for Multi-class Support Vector Machines. 2005. [View Context].
Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD Conference. 2005. [View Context].
Jeroen Eggermont and Joost N. Kok and Walter A. Kosters. Genetic Programming for data classification: partitioning the search space. SAC. 2004. [View Context].
Remco R. Bouckaert and Eibe Frank. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. PAKDD. 2004. [View Context].
Mikhail Bilenko and Sugato Basu and Raymond J. Mooney. Integrating constraints and metric learning in semi-supervised clustering. ICML. 2004. [View Context].
Qingping Tao Ph. D. MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements. 2004. [View Context].
Yuan Jiang and Zhi-Hua Zhou. Editing Training Data for kNN Classifiers with Neural Network Ensemble. ISNN (1). 2004. [View Context].
Sugato Basu. Semi-Supervised Clustering with Limited Background Knowledge. AAAI. 2004. [View Context].
Judith E. Devaney and Steven G. Satterfield and John G. Hagedorn and John T. Kelso and Adele P. Peskin and William George and Terence J. Griffin and Howard K. Hung and Ronald D. Kriz. Science at the Speed of Thought. Ambient Intelligence for Scientific Discovery. 2004. [View Context].
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Sugato Basu. Also Appears as Technical Report, UT-AI. PhD Proposal. 2003. [View Context].
Dick de Ridder and Olga Kouropteva and Oleg Okun and Matti Pietikäinen and Robert P W Duin. Supervised Locally Linear Embedding. ICANN. 2003. [View Context].
Aristidis Likas and Nikos A. Vlassis and Jakob J. Verbeek. The global k-means clustering algorithm. Pattern Recognition, 36. 2003. [View Context].
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Julie Greensmith. New Frontiers For An Artificial Immune System. Digital Media Systems Laboratory HP Laboratories Bristol. 2003. [View Context].
Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Fast hierarchical clustering and its validation. Data Knowl. Eng, 44. 2003. [View Context].
Bob Ricks and Dan Ventura. Training a Quantum Neural Network. NIPS. 2003. [View Context].
Eibe Frank and Mark Hall. Visualizing Class Probability Estimators. PKDD. 2003. [View Context].
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Michail Vlachos and Carlotta Domeniconi and Dimitrios Gunopulos and George Kollios and Nick Koudas. Non-linear dimensionality reduction techniques for classification and visualization. KDD. 2002. [View Context].
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Inderjit S. Dhillon and Dharmendra S. Modha and W. Scott Spangler. Class visualization of high-dimensional data with applications. Department of Computer Sciences, University of Texas. 2002. [View Context].
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Ayhan Demiriz and Kristin P. Bennett and Mark J. Embrechts. A Genetic Algorithm Approach for Semi-Supervised Clustering. E-Business Department, Verizon Inc.. 2002. [View Context].
Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [View Context].
Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao and Limsoon Wong. DeEPs: A New Instance-based Discovery and Classification System. Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases. 2001. [View Context].
David Hershberger and Hillol Kargupta. Distributed Multivariate Regression Using Wavelet-Based Collective Data Mining. J. Parallel Distrib. Comput, 61. 2001. [View Context].
David Horn and A. Gottlieb. The Method of Quantum Clustering. NIPS. 2001. [View Context].
Asa Ben-Hur and David Horn and Hava T. Siegelmann and Vladimir Vapnik. A Support Vector Method for Clustering. NIPS. 2000. [View Context].
Neil Davey and Rod Adams and Mary J. George. The Architecture and Performance of a Stochastic Competitive Evolutionary Neural Tree Network. Appl. Intell, 12. 2000. [View Context].
Edgar Acuna and Alex Rojas. Ensembles of classifiers based on Kernel density estimators. Department of Mathematics University of Puerto Rico. 2000. [View Context].
Manoranjan Dash and Huan Liu. Feature Selection for Clustering. PAKDD. 2000. [View Context].
Carlotta Domeniconi and Jing Peng and Dimitrios Gunopulos. An Adaptive Metric Machine for Pattern Classification. NIPS. 2000. [View Context].
David M J Tax and Robert P W Duin. Support vector domain description. Pattern Recognition Letters, 20. 1999. [View Context].
Ismail Taha and Joydeep Ghosh. Symbolic Interpretation of Artificial Neural Networks. IEEE Trans. Knowl. Data Eng, 11. 1999. [View Context].
Foster J. Provost and Tom Fawcett and Ron Kohavi. The Case against Accuracy Estimation for Comparing Induction Algorithms. ICML. 1998. [View Context].
Stephen D. Bay. Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets. ICML. 1998. [View Context].
Wojciech Kwedlo and Marek Kretowski. Discovery of Decision Rules from Databases: An Evolutionary Approach. PKDD. 1998. [View Context].
Igor Kononenko and Edvard Simec and Marko Robnik-Sikonja. Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF. Appl. Intell, 7. 1997. [View Context].
. Prototype Selection for Composite Nearest Neighbor Classifiers. Department of Computer Science University of Massachusetts. 1997. [View Context].
Ke Wang and Han Chong Goh. Minimum Splits Based Discretization for Continuous Features. IJCAI (2). 1997. [View Context].
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Lawrence O. Hall and Nitesh V. Chawla and Kevin W. Bowyer. Decision Tree Learning on Very Large Data Sets. Department of Computer Science and Engineering, ENB 118 University of South Florida. [View Context].
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Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [View Context].
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