Data Mining and Knowledge Discovery Handbook, 2 Edition part 77 pps
Data Mining and Knowledge Discovery Handbook, 2 Edition part 77 pps
... Predictors. Machine Learning 1996; 24 (2) : 123 -140. Breiman, L. Randomizing Outputs to Increase Prediction Accuracy. Machine Learning 20 00; 40(3) :22 9 -24 2. Dietterich, T.G. and Kong, E.B. Machine Learning ... Curse-of-Dimensionality. Data Mining and Knowledge Discovery 1997; 1:55 -77. Geman, S., Bienenstock, E. and Doursat, R. Neural Networks and the Bias/Vari-ance Di...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 1 pps
... Rokach Editors Data Mining and Knowledge Discovery Handbook Second Edition 123 Contents 1 Introduction to Knowledge Discovery and Data Mining Oded Maimon, Lior Rokach 1 Part I Preprocessing Methods 2 Data ... neural networks, and evolutionary algorithms. Parts five and six present supporting and advanced methods in Data Mining, such as statistical methods...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 4 ppsx
... in data mining, Data Mining and Knowledge Discovery, 15(1):87-97, 20 07. Larose, D.T., Discovering knowledge in data: an introduction to data mining, John Wiley and Sons, 20 05. Maimon O., and ... Pub, 20 05. Wu, X. and Kumar, V. and Ross Quinlan, J. and Ghosh, J. and Yang, Q. and Motoda, H. and McLachlan, G.J. and Ng, A. and Liu, B. and Yu, P.S. an...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 7 ppsx
... Foundations and New Directions in Data Mining, as- sociated with the third IEEE International Conference on Data Mining, Melbourne, FL, November 1 922 , 24 –30, 20 03A. Dardzinska A. and Ras Z.W. On rule discovery ... (Dardzinska and Ras, 20 03A,Dardzinska and Ras, 20 03B). Learning missing attribute values from summary constraints was reported in (Wu and Barbara, 20 02...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 12 ppsx
... Springer, pp. 178-196, 20 02. Maimon, O. and Rokach, L., Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications, Series in Machine Perception and Artificial In- telligence ... Kaufmann, 1996. Maimon O., and Rokach, L. Data Mining by Attribute Decomposition with semiconductors manufacturing case study, in Data Mining for Design and...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 16 ppsx
... Conference on Data- mining (ICDM’ 02) , Maebashi City, Japan, CSIRO Technical Report CMIS- 02/ 1 02, 20 02. Williams G. J., Huang Z., Mining the knowledge mine: The hot spots methodology for mining large ... phenomena). When data is limited, it is common practice to re-sample the data, that is, partition the data into training and test sets in different ways. An inducer is tr...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 17 ppsx
... 131–158. Rokach, L. and Maimon, O., Clustering methods, Data Mining and Knowledge Discovery Handbook, pp. 321 –3 52, 20 05, Springer. Rokach, L. and Maimon, O., Data mining for improving the quality of manufacturing: ... 14: 2, 24 1-301, 20 02. Shafer, J. C., Agrawal, R. and Mehta, M. , SPRINT: A Scalable Parallel Classifier for Data Mining, Proc. 22 nd Int. Conf...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 22 pps
... (Sebastiani and Ramoni, 20 00, Sebastiani and Ramoni, 20 01B) to customer profiling (Sebastiani et al., 20 00) and bioinformatics (Friedman, 20 04,Sebastiani et al., 20 04 ,2) . Here we describe two Data Mining ... = ∑ i {log( σ 2 i0 / σ 2 i0 ) −(y i − μ i1 ) 2 / σ 2 i1 +(y i − μ i0 ) 2 / σ 2 i0 } where y i is the value of attribute i in the new sample to classify a...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 30 ppsx
... as: d(x i ,x j )=(w 1 x i1 −x j1 g + w 2 x i2 −x j2 g + +w p x ip −x jp g ) 1/g where w i ∈ [0,∞) 27 8 Lior Rokach can be interpreted as agreements, and b and c as disagreements. The Rand index is defined as: RAND ... + b + c + d The Rand index lies between 0 and 1. When the two partitions agree perfectly, the Rand index is 1. A problem with the Rand index is t...
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Data Mining and Knowledge Discovery Handbook, 2 Edition part 31 pps
... parameter vector and then alternates between two steps (Farley and Raftery, 1998): an “E-step”, in which the conditional expectation of the complete data likelihood given the observed data and the current ... Bernoulli, Poisson, and log-normal distributions (Cheese- man and Stutz, 1996). Other well-known density-based methods include: SNOB (Wallace and Dowe, 1994) and MCLUST...
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