Data Mining and Knowledge Discovery Handbook, 2 Edition part 7 ppsx

... 5 ,7} = {4},KA(5)={1,4,5}∩ {2, 4,5, 7} = {4,5},KA(6)={6 ,7} ∩ {2, 4,6,8}∩{1, 3, 6} = {6},KA (7) ={6 ,7} ∩{3 ,7} ∩ {2, 4,5, 7} = {7} , and KA(8)= {2, 4,6,8}. and for Table 3.15 and B = A,KA(1)={1,3,4,5,8}∩{1, ... is known and ρ(x,a)=v. For Table3.1 and B = A,KA(1)={1,4,5}∩{1,3,6} = {1},KA (2) = {2} ∩ {2, 4,6,8}∩ {2, 4, 5, 7} = {2} ,KA(3)={3 ,7} ∩{1,3,6} = {3},KA(4)={1,4,5}∩ {2, 4,6, 8}∩ {2, 4, 5 ,7} = {4},KA(5)={1,4,5}∩ {2, 4,5, ... 8},KA (2) = {2, 3,8}∩{1 ,2, 4, 5, 6, 8}∩ {2, 4,5 ,7, 8}= {2, 8},KA(3)={1,3,5 ,7} ∩{1,3, 6, 8} = {1,3},KA(4)={1,3,4,5,8}∩{1, 2, 4, 5,6, 8}∩ {2, 4,5 ,7, 8}= {4,5,8},KA(5)={1,3,4,5,8}∩ {2, 4, 5, 7, 8} =...

... 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 ... Science, Volume 177 , Issue 17, pp. 35 92- 36 12, 20 07. Hastie, T. and Tibshirani, R. and Friedman, J. and Franklin, J., The elements of statisticallearning: data mining, inference and prediction, ... 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. and others, Top 10 algorithms in data mining, ...

... 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 ... lr7,l10 and Medicine lr2,lr9, and for many data mining techniques, such as: decision trees lr6,lr 12, lr15, clustering lr13,lr8, ensemblemethods lr1,lr4,lr5,lr16 and genetic algorithms lr 17, lr11.ReferencesAha, ... ISBN:981 -25 6- 079 -3, 20 05.Mallows, C. L. Some comments on Cp . Technometrics 15, 661- 676 , 1 973 Michalski, R. S. A theory and methodology of inductive learning. Artificial Intelligence, 20 ( 2) : 111–...

... Maimon, L. Rokach (eds.), Data Mining and Knowledge Discovery Handbook, 2nd ed., DOI 10.10 07/ 978 -0-3 87- 09 823 -4_8, © Springer Science+Business Media, LLC 20 10 Department of Industrial Engineering, ... 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, partitionthe data into training and test sets in different ways. An inducer is trained and testedfor each partition and...

... 175 7- 177 1, 20 04.Buja, A. and Lee, Y.S., Data Mining criteria for tree based regression and classification, Pro-ceedings of the 7th International Conference on Knowledge Discovery and Data Mining, (pp ... 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. Very Large Databases, T. M. Vijayaraman and AlejandroP....

... as:d(xi,xj)=(w1xi1−xj1g+ w 2 xi2−xj2g+ +wpxip−xjpg)1/gwhere wi∈ [0,∞) 27 8 Lior Rokachcan be interpreted as agreements, and b and c as disagreements. The Rand index isdefined as:RAND ... cluster in C 2 , but not in the same cluster in C1; and d be the number of pairs ofinstances that are assigned to different clusters in C1 and C 2 . The quantities a and d 27 6 Lior RokachJd=|SW|=K∑k=1Sk• ... structure (Larsen and Aone, 1999).Rand IndexThe Rand index (Rand, 1 971 ) is a simple criterion used to compare an induced clus-tering structure (C1) with a given clustering structure (C 2 ). Let a...