Center for Machine
Learning and Intelligent Systems

About  Citation
Policy  Donate a Data
Set  Contact

Repository Web

View ALL Data Sets

Iris Data Set
Download: Data Folder, Data Set
Description

Abstract: Famous database; from Fisher,
1936

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:

33705

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,
[email-masked] '@' ). 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:

Anthony K H Tung and Xin Xu and Beng Chin Ooi. CURLER:
Finding and Visualizing Nonlinear Correlated Clusters. SIGMOD
Conference. 2005. [View
Context].

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].

Igor Fischer and Jan Poland. Amplifying
the Block Matrix Structure for Spectral Clustering. Telecommunications
Lab. 2005. [View
Context].

Sotiris B. Kotsiantis and Panayiotis E. Pintelas. Logitboost
of Simple Bayesian Classifier. Informatica. 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].

Sugato Basu. Semi-Supervised
Clustering with Limited Background Knowledge. AAAI. 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].

Mikhail Bilenko and Sugato Basu and Raymond J.
Mooney. Integrating
constraints and metric learning in semi-supervised clustering. ICML.
2004. [View
Context].

Jennifer G. Dy and Carla Brodley. Feature
Selection for Unsupervised Learning. Journal of Machine Learning
Research, 5. 2004. [View
Context].

Yuan Jiang and Zhi-Hua Zhou. Editing
Training Data for kNN Classifiers with Neural Network Ensemble. ISNN
(1). 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].

Remco R. Bouckaert and Eibe Frank. Evaluating
the Replicability of Significance Tests for Comparing Learning
Algorithms. PAKDD. 2004. [View
Context].

Eibe Frank and Mark Hall. Visualizing
Class Probability Estimators. PKDD. 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].

Aristidis Likas and Nikos A. Vlassis and Jakob J.
Verbeek. The
global k-means clustering algorithm. Pattern Recognition, 36. 2003.
[View
Context].

Sugato Basu. Also
Appears as Technical Report, UT-AI. PhD Proposal. 2003. [View
Context].

Bob Ricks and Dan Ventura. Training
a Quantum Neural Network. NIPS. 2003. [View
Context].

Zhi-Hua Zhou and Yuan Jiang and Shifu Chen. Extracting
symbolic rules from trained neural network ensembles. AI Commun, 16.
2003. [View
Context].

Ross J. Micheals and Patrick Grother and P. Jonathon
Phillips. The
NIST HumanID Evaluation Framework. AVBPA. 2003. [View
Context].

Dick de Ridder and Olga Kouropteva and Oleg Okun and
Matti Pietik鋓nen and Robert P W Duin. Supervised
Locally Linear Embedding. ICANN. 2003. [View
Context].

Jeremy Kubica and Andrew Moore. Probabilistic
Noise Identification and Data Cleaning. ICDM. 2003. [View
Context].

Julie Greensmith. New
Frontiers For An Artificial Immune System. Digital Media Systems
Laboratory HP Laboratories Bristol. 2003. [View
Context].

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].

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].

Manoranjan Dash and Kiseok Choi and Peter Scheuermann
and Huan Liu. Feature
Selection for Clustering - A Filter Solution. ICDM. 2002. [View
Context].

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].

Geoffrey Holmes and Bernhard Pfahringer and Richard
Kirkby and Eibe Frank and Mark A. Hall. Multiclass
Alternating Decision Trees. ECML. 2002. [View
Context].

Jun Wang and Bin Yu and Les Gasser. Concept
Tree Based Clustering Visualization with Shaded Similarity Matrices.
ICDM. 2002. [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].

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].

Edgar Acuna and Alex Rojas. Ensembles
of classifiers based on Kernel density estimators. Department of
Mathematics University of Puerto Rico. 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].

Asa Ben-Hur and David Horn and Hava T. Siegelmann and
Vladimir Vapnik. A
Support Vector Method for Clustering. NIPS. 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].

Wojciech Kwedlo and Marek Kretowski. Discovery
of Decision Rules from Databases: An Evolutionary Approach. PKDD.
1998. [View
Context].

Stephen D. Bay. Combining
Nearest Neighbor Classifiers Through Multiple Feature Subsets. ICML.
1998. [View
Context].

Foster J. Provost and Tom Fawcett and Ron Kohavi. The
Case against Accuracy Estimation for Comparing Induction Algorithms.
ICML. 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].

Ethem Alpaydin. Voting
over Multiple Condensed Nearest Neighbors. Artif. Intell. Rev, 11.
1997. [View
Context].

Ke Wang and Han Chong Goh. Minimum
Splits Based Discretization for Continuous Features. IJCAI (2). 1997.
[View
Context].

. Prototype
Selection for Composite Nearest Neighbor Classifiers. Department of
Computer Science University of Massachusetts. 1997. [View
Context].

Tapio Elomaa and Juho Rousu. Finding
Optimal Multi-Splits for Numerical Attributes in Decision Tree
Learning. ESPRIT Working Group in Neural and Computational Learning.
1996. [View
Context].

Ron Kohavi. Scaling
Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
KDD. 1996. [View
Context].

Daniel C. St and Ralph W. Wilkerson and Cihan H.
Dagli. RULE
SET QUALITY MEASURES FOR INDUCTIVE LEARNING ALGORITHMS. proceedings of
the Artificial Neural Networks In Engineering Conference 1996 (ANNIE.
1996. [View
Context].

Ron Kohavi. The
Power of Decision Tables. ECML. 1995. [View
Context].

Ron Kohavi. A
Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model
Selection. IJCAI. 1995. [View
Context].

George H. John and Ron Kohavi and Karl Pfleger. Irrelevant
Features and the Subset Selection Problem. ICML. 1994. [View
Context].

Zoubin Ghahramani and Michael I. Jordan. Learning
from incomplete data. MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL
INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL
LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES. 1994. [View
Context].

Gabor Melli. A
Lazy Model-Based Approach to On-Line Classification. University of
British Columbia. 1989. [View
Context].

Karthik Ramakrishnan. UNIVERSITY
OF MINNESOTA. [View
Context].

Wl/odzisl/aw Duch and Rafal Adamczak and Geerd H. F
Diercksen. Neural
Networks from Similarity Based Perspective. Department of Computer
Methods, Nicholas Copernicus University. [View
Context].

G. Ratsch and B. Scholkopf and Alex Smola and
Sebastian Mika and T. Onoda and K. -R Muller. Robust
Ensemble Learning for Data Mining. GMD FIRST, Kekul#estr. [View
Context].

Aynur Akku and H. Altay Guvenir. Weighting
Features in k Nearest Neighbor Classification on Feature Projections.
Department of Computer Engineering and Information Science Bilkent
University. [View
Context].

Wl odzisl/aw Duch and Rafal Adamczak and Norbert
Jankowski. Initialization
of adaptive parameters in density networks. Department of Computer
Methods, Nicholas Copernicus University. [View
Context].

Jun Wang. Classification
Visualization with Shaded Similarity Matrix. Bei Yu Les Gasser
Graduate School of Library and Information Science University of Illinois
at Urbana-Champaign. [View
Context].

Eric P. Kasten and Philip K. McKinley. MESO:
Perceptual Memory to Support Online Learning in Adaptive Software.
Proceedings of the Third International Conference on Development and
Learning (ICDL. [View
Context].

Karol Grudzi nski and Wl/odzisl/aw Duch. SBL-PM:
A Simple Algorithm for Selection of Reference Instances in Similarity
Based Methods. Department of Computer Methods, Nicholas Copernicus
University. [View
Context].

G. Ratsch and B. Scholkopf and Alex Smola and K. -R
Muller and T. Onoda and Sebastian Mika. Arc:
Ensemble Learning in the Presence of Outliers. GMD FIRST. [View
Context].

Wl odzisl/aw Duch and Rudy Setiono and Jacek M.
Zurada. Computational
intelligence methods for rule-based data understanding. [View
Context].

Takao Mohri and Hidehiko Tanaka. An
Optimal Weighting Criterion of Case Indexing for Both Numeric and Symbolic
Attributes. Information Engineering Course, Faculty of Engineering The
University of Tokyo. [View
Context].

Fran ois Poulet. Cooperation
between automatic algorithms, interactive algorithms and visualization
tools for Visual Data Mining. ESIEA Recherche. [View
Context].

Fernando Fern#andez and Pedro Isasi. Designing
Nearest Neighbour Classifiers by the Evolution of a Population of
Prototypes. Universidad Carlos III de Madrid. [View
Context].

Maria Salamo and Elisabet Golobardes. Analysing
Rough Sets weighting methods for Case-Based Reasoning Systems.
Enginyeria i Arquitectura La Salle. [View
Context].

YongSeog Kim and W. Nick Street and Filippo Menczer.
Optimal
Ensemble Construction via Meta-Evolutionary Ensembles. Business
Information Systems, Utah State University. [View
Context].

Andrew Watkins and Jon Timmis and Lois C. Boggess. Artificial
Immune Recognition System (AIRS): An ImmuneInspired Supervised Learning
Algorithm. (abw5,[email-masked]) Computing Laboratory, University of
Kent. [View
Context].

Gaurav Marwah and Lois C. Boggess. Artificial
Immune Systems for Classification : Some Issues. Department of
Computer Science Mississippi State University. [View
Context].

H. Altay G uvenir and Aynur Akkus. WEIGHTED
K NEAREST NEIGHBOR CLASSIFICATION ON FEATURE PROJECTIONS. Department
of Computer Engineering and Information Science Bilkent University. [View
Context].

Michael R. Berthold and Klaus--Peter Huber. From
Radial to Rectangular Basis Functions: A new Approach for Rule Learning
from Large Datasets. Institut fur Rechnerentwurf und Fehlertoleranz
(Prof. D. Schmid) Universitat Karlsruhe. [View
Context].

Wl odzisl/aw Duch and Karol Grudzinski. Prototype
based rules - a new way to understand the data. Department of Computer
Methods, Nicholas Copernicus University. [View
Context].

H. Altay Guvenir. A
Classification Learning Algorithm Robust to Irrelevant Features.
Bilkent University, Department of Computer Engineering and Information
Science. [View
Context].

Stefan Aeberhard and Danny Coomans and De Vel. THE
PERFORMANCE OF STATISTICAL PATTERN RECOGNITION METHODS IN HIGH DIMENSIONAL
SETTINGS. James Cook University. [View
Context].

Huan Li and Wenbin Chen. Supervised
Local Tangent Space Alignment for Classification. I-Fan Shen. [View
Context].

Adam H. Cannon and Lenore J. Cowen and Carey E.
Priebe. Approximate
Distance Classification. Department of Mathematical Sciences The Johns
Hopkins University. [View
Context].

Asa Ben-Hur and David Horn and Hava T. Siegelmann and
Vladimir Vapnik. A
Support Vector Method for Hierarchical Clustering. Faculty of IE and
Management Technion. [View
Context].

Lawrence O. Hall and Nitesh V. Chawla and Kevin W.
Bowyer. Combining
Decision Trees Learned in Parallel. Department of Computer Science and
Engineering, ENB 118 University of South Florida. [View
Context].

Igor Kononenko and Edvard Simec. Induction
of decision trees using RELIEFF. University of Ljubljana, Faculty of
electrical engineering & computer science. [View
Context].

Chih-Wei Hsu and Cheng-Ru Lin. A
Comparison of Methods for Multi-class Support Vector Machines.
Department of Computer Science and Information Engineering National Taiwan
University. [View
Context].

Huan Liu. A
Family of Efficient Rule Generators. Department of Information Systems
and Computer Science National University of Singapore. [View
Context].

Norbert Jankowski. Survey
of Neural Transfer Functions. Department of Computer Methods, Nicholas
Copernicus University. [View
Context].

Enes Makalic and Lloyd Allison and David L. Dowe. MML
INFERENCE OF SINGLE-LAYER NEURAL NETWORKS. School of Computer Science
and Software Engineering Monash University. [View
Context].

Ron Kohavi and Brian Frasca. Useful
Feature Subsets and Rough Set Reducts. the Third International
Workshop on Rough Sets and Soft Computing. [View
Context].

Ping Zhong and Masao Fukushima. Second
Order Cone Programming Formulations for Robust Multi-class
Classification. [View
Context].

Michael P. Cummings and Daniel S. Myers and Marci
Mangelson. Applying
Permuation Tests to Tree-Based Statistical Models: Extending the R Package
rpart. Center for Bioinformatics and Computational Biology, Institute
for Advanced Computer Studies, University of Maryland. [View
Context].

A. da Valls and Vicen Torra. Explaining
the consensus of opinions with the vocabulary of the experts. Dept.
d'Enginyeria Informtica i Matemtiques Universitat Rovira i Virgili. [View
Context].

Wl/odzisl/aw Duch and Rafal Adamczak and Krzysztof
Grabczewski. Extraction
of crisp logical rules using constrained backpropagation networks.
Department of Computer Methods, Nicholas Copernicus University. [View
Context].

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].

Geoffrey Holmes and Leonard E. Trigg. A
Diagnostic Tool for Tree Based Supervised Classification Learning
Algorithms. Department of Computer Science University of Waikato
Hamilton New Zealand. [View
Context].

Anthony Robins and Marcus Frean. Learning
and generalisation in a stable network. Computer Science, The
University of Otago. [View
Context].

Shlomo Dubnov and Ran El and Yaniv Technion and Yoram
Gdalyahu and Elad Schneidman and Naftali Tishby and Golan Yona. Clustering
By Friends : A New Nonparametric Pairwise Distance Based Clustering
Algorithm. Ben Gurion University. [View
Context].

Daichi Mochihashi and Gen-ichiro Kikui and Kenji
Kita. Learning
Nonstructural Distance Metric by Minimum Cluster Distortions. ATR
Spoken Language Translation research laboratories. [View
Context].

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].

Wl/odzisl/aw Duch and Rafal Adamczak and Geerd H. F
Diercksen. Classification,
Association and Pattern Completion using Neural Similarity Based
Methods. Department of Computer Methods, Nicholas Copernicus
University. [View
Context].

Wl odzisl and Rafal Adamczak and Krzysztof
Grabczewski and Grzegorz Zal. A
hybrid method for extraction of logical rules from data. Department of
Computer Methods, Nicholas Copernicus University. [View
Context].

Rudy Setiono and Huan Liu. Fragmentation
Problem and Automated Feature Construction. School of Computing
National University of Singapore. [View
Context].

Citation Request:

Please refer to the Machine Learning Repository's citation
policy

[1] Papers were automatically harvested and
associated with this data set, in collaboration with Rexa.info

Supported By:

In Collaboration With:

About  ||  Citation
Policy  ||  Donation
Policy  ||  Contact  ||
CML