Proceedings of the Fourth European Conference on Principles and Practice of Knowledge Discovery in Databases. DeEPs: A New Instance-based Discovery and Classification System. Jinyan Li and Guozhu Dong and Kotagiri Ramamohanarao and Limsoon Wong. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. Non-linear dimensionality reduction techniques for classification and visualization. Michail Vlachos and Carlotta Domeniconi and Dimitrios Gunopulos and George Kollios and Nick Koudas. Concept Tree Based Clustering Visualization with Shaded Similarity Matrices. A Genetic Algorithm Approach for Semi-Supervised Clustering. Feature Selection for Clustering - A Filter Solution. Manoranjan Dash and Kiseok Choi and Peter Scheuermann and Huan Liu.
Department of Computer Sciences, University of Texas. Class visualization of high-dimensional data with applications. Geoffrey Holmes and Bernhard Pfahringer and Richard Kirkby and Eibe Frank and Mark A. Digital Media Systems Laboratory HP Laboratories Bristol. New Frontiers For An Artificial Immune System. Probabilistic Noise Identification and Data Cleaning. Extracting symbolic rules from trained neural network ensembles. Zhi-Hua Zhou and Yuan Jiang and Shifu Chen. 2003.ĭick de Ridder and Olga Kouropteva and Oleg Okun and Matti Pietikäinen and Robert P W Duin. Visualizing Class Probability Estimators. Fast hierarchical clustering and its validation. Manoranjan Dash and Huan Liu and Peter Scheuermann and Kian-Lee Tan. Editing Training Data for kNN Classifiers with Neural Network Ensemble. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements.
MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Integrating constraints and metric learning in semi-supervised clustering. Mikhail Bilenko and Sugato Basu and Raymond J. Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms. Genetic Programming for data classification: partitioning the search space. Feature Selection for Unsupervised Learning. Ambient Intelligence for Scientific Discovery. Semi-Supervised Clustering with Limited Background Knowledge. Logitboost of Simple Bayesian Classifier. Amplifying the Block Matrix Structure for Spectral Clustering. CURLER: Finding and Visualizing Nonlinear Correlated Clusters. 2005.Īnthony K H Tung and Xin Xu and Beng Chin Ooi. A Regularized Nonsmooth Newton Method for Multi-class Support Vector Machines. 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. IEEE Transactions on Information Theory, May 1972, 431-433. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". (1973) Pattern Classification and Scene Analysis. "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).ĭuda,R.O., & Hart,P.E. The 38th sample: 4.9,3.6,1.4,0.1,"Iris-setosa" where the errors are in the second and third features.įisher,R.A. The 35th sample should be: 4.9,3.1,1.5,0.2,"Iris-setosa" where the error is in the fourth feature. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick ). Predicted attribute: class of iris plant.
One class is linearly separable from the other 2 the latter are NOT linearly separable from each other. (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. Fisher's paper is a classic in the field and is referenced frequently to this day. This is perhaps the best known database to be found in the pattern recognition literature. Click here to try out the new site.ĭownload: Data Folder, Data Set DescriptionĪbstract: Famous database from Fisher, 1936 Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns.
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