How To Download Mushroom Data Set From Uci UPDATED
How To Download Mushroom Data Set From Uci
| Mushroom Data Set up Abstruse: From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible |  |
| Information Set Characteristics: | Multivariate | Number of Instances: | 8124 | Surface area: | Life |
| Attribute Characteristics: | Categorical | Number of Attributes: | 22 | Date Donated | 1987-04-27 |
| Associated Tasks: | Classification | Missing Values? | Yes | Number of Web Hits: | 713332 |
Source:
Origin:
Mushroom records drawn from The Audubon Society Field Guide to Northward American Mushrooms (1981). Grand. H. Lincoff (Pres.), New York: Alfred A. Knopf
Donor:
Jeff Schlimmer (
Jeffrey.Schlimmer '@' a.gp.cs.cmu.edu)
Information Set Information:
This data set includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family (pp. 500-525). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and non recommended. This latter grade was combined with the poisonous one. The Guide clearly states that there is no elementary rule for determining the edibility of a mushroom; no rule like ``leaflets three, permit it be'' for Poisonous Oak and Ivy.
Attribute Information:
1. cap-shape: bong=b,conical=c,convex=x,apartment=f, knobbed=k,sunken=s
2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=due south
iii. cap-color: brownish=n,buff=b,cinnamon=c,greyness=one thousand,green=r, pinkish=p,regal=u,reddish=e,white=west,yellow=y
iv. bruises?: bruises=t,no=f
5. aroma: almond=a,anise=fifty,creosote=c,fishy=y,foul=f, musty=m,none=n,pungent=p,spicy=s
6. gill-attachment: attached=a,descending=d,free=f,notched=n
7. gill-spacing: close=c,crowded=w,distant=d
8. gill-size: broad=b,narrow=n
9. gill-color: black=k,brown=due north,vitrify=b,chocolate=h,grayness=g, light-green=r,orange=o,pinkish=p,purple=u,red=e, white=w,yellow=y
10. stalk-shape: enlarging=e,tapering=t
11. stem-root: bulbous=b,society=c,cup=u,equal=e, rhizomorphs=z,rooted=r,missing=?
12. stalk-surface-above-ring: fibrous=f,scaly=y,silky=thou,smooth=s
xiii. stalk-surface-below-ring: fibrous=f,scaly=y,silky=k,smoothen=due south
14. stalk-colour-above-ring: brown=n,buff=b,cinnamon=c,gray=g,orangish=o, pink=p,red=due east,white=w,xanthous=y
xv. stalk-color-below-ring: brown=northward,buff=b,cinnamon=c,grey=g,orangish=o, pink=p,red=e,white=due west,yellowish=y
16. veil-type: fractional=p,universal=u
17. veil-color: brownish=north,orange=o,white=westward,yellowish=y
18. ring-number: none=n,ane=o,two=t
xix. ring-blazon: cobwebby=c,evanescent=e,flaring=f,large=l, none=n,pendant=p,sheathing=southward,zone=z
20. spore-impress-color: black=k,brown=n,buff=b,chocolate=h,green=r, orange=o,imperial=u,white=due west,yellow=y
21. population: abundant=a,amassed=c,numerous=n, scattered=due south,several=five,solitary=y
22. habitat: grasses=1000,leaves=l,meadows=m,paths=p, urban=u,waste=westward,wood=d
Relevant Papers:
Schlimmer,J.South. (1987). Concept Acquisition Through Representational Adjustment (Technical Report 87-nineteen). Doctoral disseration, Department of Information and Computer Scientific discipline, Academy of California, Irvine.
[Web Link]
Iba,W., Wogulis,J., & Langley,P. (1988). Trading off Simplicity and Coverage in Incremental Concept Learning. In Proceedings of the 5th International Conference on Machine Learning, 73-79. Ann Arbor, Michigan: Morgan Kaufmann.
[Web Link]
Duch West, Adamczak R, Grabczewski K (1996) Extraction of logical rules from preparation data using backpropagation networks, in: Proc. of the The 1st Online Workshop on Soft Computing, 19-xxx.Aug.1996, pp. 25-30, [Spider web Link]
[Spider web Link]
Duch W, Adamczak R, Grabczewski Grand, Ishikawa M, Ueda H, Extraction of crisp logical rules using constrained backpropagation networks - comparing of two new approaches, in: Proc. of the European Symposium on Bogus Neural Networks (ESANN'97), Bruge, Kingdom of belgium sixteen-18.4.1997.
[Web Link]
Papers That Cite This Data Setone:
Manuel Oliveira. Library Release Form Proper 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].
Hyunsoo Kim and Se Hyun Park. Data Reduction in Support Vector Machines by a Kernelized Ionic Interaction Model. SDM. 2004. [View Context].
Xiaoyong Chai and Li Deng and Qiang Yang and Charles Ten. Ling. Exam-Cost Sensitive Naive Bayes Classification. ICDM. 2004. [View Context].
Daniel J. Lizotte and Omid Madani and Russell Greiner. Approaching Learning of Naive-Bayes Classifiers. UAI. 2003. [View Context].
Daniel Barbar and Yi Li and Julia Couto. COOLCAT: an entropy-based algorithm for categorical clustering. CIKM. 2002. [View Context].
Stephen D. Bay and Michael J. Pazzani. Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov, 5. 2001. [View Context].
Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao and Limsoon Wong. DeEPs: A New Instance-based Discovery and Nomenclature System. Proceedings of the 4th European Briefing on Principles and Do of Noesis Discovery in Databases. 2001. [View Context].
Huan Liu and Hongjun Lu and Jie Yao. Toward Multidatabase Mining: Identifying Relevant Databases. IEEE Trans. Knowl. Data Eng, xiii. 2001. [View Context].
Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao. Example-Based Classification by Emerging Patterns. PKDD. 2000. [View Context].
Farhad Hussain and Huan Liu and Einoshin Suzuki and Hongjun Lu. Exception Dominion Mining with a Relative Interestingness Mensurate. PAKDD. 2000. [View Context].
Kiri Wagstaff and Claire Cardie. Clustering with Example-level Constraints. ICML. 2000. [View Context].
Marking A. Hall and Lloyd A. Smith. Characteristic Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. FLAIRS Conference. 1999. [View Context].
Jinyan Li and Xiuzhen Zhang and Guozhu Dong and Kotagiri Ramamohanarao and Qun Sunday. Efficient Mining of High Confidience Clan Rules without Support Thresholds. PKDD. 1999. [View Context].
Seth Bullock and Peter K. Todd. Fabricated to Measure: Ecological Rationality in Structured Environments. Heart for Adaptive Behavior and Cognition Max Planck Plant for Man Development. 1999. [View Context].
Venkatesh Ganti and Johannes Gehrke and Raghu Ramakrishnan. CACTUS - Clustering Chiselled Data Using Summaries. KDD. 1999. [View Context].
Ismail Taha and Joydeep Ghosh. Symbolic Interpretation of Bogus Neural Networks. IEEE Trans. Knowl. Data Eng, 11. 1999. [View Context].
Mark A. Hall. Section of Computer Science Hamilton, NewZealand Correlation-based Feature Selection for Auto Learning. Doctor of Philosophy at The University of Waikato. 1999. [View Context].
Huan Liu and Hongjun Lu and Ling Feng and Farhad Hussain. Efficient Search of Reliable Exceptions. PAKDD. 1999. [View Context].
Huan Liu and Rudy Setiono. Incremental Feature Choice. Appl. Intell, nine. 1998. [View Context].
Nicholas Howe and Claire Cardie. Examining Locally Varying Weights for Nearest Neighbor Algorithms. ICCBR. 1997. [View Context].
Robert K French. Pseudo-recurrent connectionist networks: An arroyo to the "sensitivity-stability" dilemma.. Connection Science. 1997. [View Context].
Guszti Bartfai. VICTORIA UNIVERSITY OF WELLINGTON Te Whare Wananga o te Upoko o te Ika a Maui. Department of Computer Scientific discipline PO Box 600. 1996. [View Context].
Huan Liu and Rudy Setiono. A Probabilistic Approach to Feature Option - A Filter Solution. ICML. 1996. [View Context].
Kamal Ali and Michael J. Pazzani. Error Reduction through Learning Multiple Descriptions. Motorcar Learning, 24. 1996. [View Context].
Geoffrey I. Webb. OPUS: An Efficient Admissible Algorithm for Unordered Search. J. Artif. Intell. Res. (JAIR, three. 1995. [View Context].
Ron Kohavi and Barry 1000. Becker and Dan Sommerfield. Improving Uncomplicated Bayes. Data Mining and Visualization Group Silicon Graphics, Inc. [View Context].
Wl odzisl/aw Duch and Rafal Adamczak and Krzysztof Grabczewski and Norbert Jankowski. Control and Cybernetics. Section of Computer Methods, Nicholas Copernicus Academy. [View Context].
Huan Liu. A Family of Efficient Rule Generators. Section of Information Systems and Information science National University of Singapore. [View Context].
Shi Zhong and Weiyu Tang and Taghi Thousand. Khoshgoftaar. Additional Noise Filters for Identifying Mislabeled Data. Department of Computer science and Engineering Florida Atlantic Academy. [View Context].
Chotirat Ann and Dimitrios Gunopulos. Scaling upwards the Naive Bayesian Classifier: Using Decision Trees for Feature Choice. Calculator Science Department Academy of California. [View Context].
Eric P. Kasten and Philip K. McKinley. MESO: Perceptual Retentivity to Support Online Learning in Adaptive Software. Proceedings of the Third International Conference on Evolution and Learning (ICDL. [View Context].
Stefan R uping. A Simple Method For Estimating Provisional Probabilities For SVMs. CS Department, AI Unit Dortmund University. [View Context].
Josep Roure Alcobe. Incremental Colina-Climbing Search Applied to Bayesian Network Structure Learning. Escola Universitria Politcnica de Mataro. [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 Figurer Methods, Nicholas Copernicus University. [View Context].
Jinyan Li and Kotagiri Ramamohanarao and Guozhu Dong. ICML2000 The Infinite of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms. Section of Computer science and Software Engineering, The University of Melbourne, Parkville. [View Context].
Wl/odzisl/aw Duch and Rafal Adamczak and Krzysztof Grabczewski. Extraction of crisp logical rules using constrained backpropagation networks. Section of Estimator Methods, Nicholas Copernicus University. [View Context].
Wl odzisl/aw Duch and Rudy Setiono and Jacek Yard. Zurada. Computational intelligence methods for rule-based information understanding. [View Context].
C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven Chiliad. Satterfield and John G. Hagedorn and Judith Due east. Devaney. Visualization and Information Mining in an 3D Immersive Environment: Summertime Project 2003. [View Context].
Daniel J. Lizotte. Library Release Form Name of Writer. Budgeted Learning of Naive Bayes Classifiers. [View Context].
David R. Musicant. Data MINING VIA MATHEMATICAL PROGRAMMING AND Car LEARNING. Doctor of Philosophy (Computer Sciences) UNIVERSITY. [View Context].
Sherrie L. Due west and Zijian Zheng. A BENCHMARK FOR CLASSIFIER LEARNING. Basser Department of Computer science The University of Sydney. [View Context].
Anthony Robins and Marcus Frean. Learning and generalisation in a stable network. Informatics, The University of Otago. [View Context].
Rudy Setiono. Extracting K-of-Northward Rules from Trained Neural Networks. School of Computing National University of Singapore. [View Context].
Jos'eastward Fifty. Balc'azar. Rules with Bounded Negations and the Coverage Inference Scheme. Dept. LSI, UPC. [View Context].
Mehmet Dalkilic and Arijit Sengupta. A Logic-theoretic classifier called Circle. School of Informatics Center for Genomics and BioInformatics Indiana University. [View Context].
Daniel J. Lizotte and Omid Madani and Russell Greiner. Budgeted Learning, Part II: The Na#ve-Bayes Example. Department of Computing Science University of Alberta. [View Context].
Citation Request:
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[one] Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info
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