A SURVEY OF DATA MINING AND KNOWLEDGE DISCOVERY PROCESS MODELS AND METHODOLOGIES
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART PPSX
53.3.1 Grid-Based Data Mining SystemsWhereas some high-performance PDKD systems have been proposed (Kargupta and Chan,2000) - see also (Cannataro et al., 2001) - there are few projects attempting to implementand/or support knowledge discovery processes over computa[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 1 PPS
Data Mining and Knowledge Discovery HandbookSecond EditionOded Maimon · Lior RokachEditorsData Mining and KnowledgeDiscovery HandbookSecond Edition123EditorsProf. Oded MaimonTel Aviv UniversityDept. Industrial Engineering69978 Ramat AvivIsraelmaimon@e[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 14 DOC
FD uses the domain knowledge to define its linguistic membership functions.When dealing with data without such domain knowledge, fuzzy borders can stillbe set up with commonly used functions such as linear, polynomial and arctan, tofuzzify the sharp borders (Wu, 1999). Wu[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 12 PPSX
further feature selection is attempted for it). The feature selection process continuesuntil all instances are inactive.Experiments on a selection of machine learning datasets showed that RC out-performed standard wrapper feature selectors using forward and backward searchstrate[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 13 POT
low any number of intervals to be merged instead of only 2 as ChiMerge does. BothChiMerge and StatDisc are based on a statistical measure of dependency. The statis-tical measures treat an attribute and a class symmetrically. A third merge discretiza-t[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 15 DOC
Finally, Penny and Jolliffe (2001) conduct a comparison study with six multivari-ate outlier detection methods. The methods’ properties are investigated by means ofa simulation study and the results indicate that no technique is superior to all oth-ers. The authors indicate seve[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 16 PPSX
8Supervised LearningLior Rokach1and Oded Maimon2Summary. This chapter summarizes the fundamental aspects of supervised methods. Thechapter provides an overview of concepts from various interrelated fields used in subsequentchapters. It presents basic definitions and arguments from[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 10 PPT
which motivates the interpretation of spectral clustering as the stationary distributionof a Markov random field: the intuition is that a random walk, once in one of themincut clusters, tends to stay in it. The stochastic interpretation also provides toolsto analyse the th[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 8 POTX
Proceedings of the 7th International Workshop on New Directions in Rough Sets, DataMining, and Granular-Soft Computing,RSFDGrC’1999, Ube, Yamaguchi, Japan, November 8–10, 1999, 73–81.Stefanowski J. and Tsoukias A. Incomplete information tables and rough classificati[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 9 PDF
Ce, which is maximized by choosing e to be theprincipal eigenvector of C, as shown above. (The other extreme case, where d= d,is easy too, since then det(ECE)=det(C) and E can be any orthogonal matrix). Werefer the reader to (Wilks, 1962) for a proof for the general case 1 &a[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 11 PDF
removed or whether discretization plays a role as well.Using One Learning Algorithm as a Filter for AnotherSeveral researchers have explored the possibility of using a particular learning algo-rithm as a pre- processor to discover useful feature subsets for a
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 22 PPS
This section describes the use of Bayesian networks to undertake other typical DataMining tasks such as classification, and for modeling more complex models, such asnonlinear and temporal dependencies.10.5.1 Bayesian Networks and ClassificationThe term “supervised cl[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 21 POT
1, ,Yv}. This ordering relation is defined by Yj Yiif Yican-not be parent of Yj. In other words, rather than exploring networks with arcs havingall possible directions, this order limits the search to a subset of networks in whichthere is only a subset of directe[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 23 DOC
households headed by a Black to be less wealthy than others, and this would be theconclusions one reaches if the gender of the head of the household is not taken intoaccount. However, the dependency structure discovered shows that the gender of thehead of th[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 20 PPT
Applications, D. Braha (ed.), Kluwer Academic Publishers, pp. 311–336, 2001.Maimon O. and Rokach L., “Improving supervised learning by feature decomposition”, Pro-ceedings of the Second International Symposium on Foundations of Information andKnowledge Systems, Lecture Notes in[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, 2 EDITION PART 24 PPT
. The signofβ3determines if the new line segment is steeper or flatter than the previous linesegment and where the new intercept falls.The process of fitting line segments to data is an example of “smoothing” a scatterplot, or applying a “smoother.” Smo[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 18 POT
Employing tightly stopping criteria tends to create small and under–fitted decisiontrees. On the other hand, using loosely stopping criteria tends to generate large de-cision trees that are over–fitted to the training set. Pruning methods originally sug-gested in (Breiman et al., 1984) were dev[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 17 PPSX
play an important role in the process of scientific discovery. A system may discoversalient features in the input data whose importance was not previously recognized. Ifthe representations formed by the inducer are comprehensible, then these discoveriescan be made a[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 19 POTX
ertheless, FDT can represent concepts with graduated characteristics by producingreal-valued outputs with gradual shiftsJanikow (1998) presented a complete framework for building a fuzzy tree includ-ing several inference procedures based on conflict resolution in rule-based systemsand e[r]
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DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK 2 EDITION PART 7 PPSX
2001).Incomplete decision tables in which all missing attribute values are ”do not care”conditions, from the view point of rough set theory, were studied for the first timein (Grzymala-Busse, 1991), where a method for rule induction was introduced inwhich each missing attribute value wa[r]
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