Data Mining Versus Semantic Web

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Major Metadata Handling Tools (for SemanticWeb) • XML • RDF • Ontology Languages • Verification (Logic +Trust) Efforts in Progress Consequently, the stress is on tools! Page 6/65
Issues in Data Mining Infrastructure Authors: Nemanja Jovanovic, nemko@acm.org Valentina Milenkovic, tina@eunet.yu Veljko Milutinovic, vm@etf.bg.ac.yu http://galeb.etf.bg.ac.yu/vm Page 7/65
Page 1 and 2: Page 1/65 Data Mining Versus Semant
Page 3 and 4: Essence of DataMining • Data and
Page 5: Major Knowledge Retrieval Algorithm
Page 9 and 10: Data Mining in the Nutshell � Unc
Page 11 and 12: Page 11/65 … A Solution � Three
Page 13 and 14: � Automated Page 13/65 The Defini
Page 15 and 16: Repetition in Solar Activity � 16
Page 17 and 18: Data Mining is Not � Data warehou
Page 19 and 20: Focus of this Presentation � Data
Page 21 and 22: Data Mining Problem Types � 6 typ
Page 23 and 24: Page 23/65 Segmentation � Separat
Page 25 and 26: Concept Description � Understanda
Page 27 and 28: Dependency Analysis � Finding the
Page 29 and 30: Neural Networks � Characterizes p
Page 31 and 32: Training Neural Networks Page 31/65
Page 33 and 34: Page 33/65 Decision Trees Balance>1
Page 35 and 36: Page 35/65 Rule Induction � Metho
Page 37 and 38: K-nearest Neighbor and Memory-Based
Page 39 and 40: Data Mining Models and Algorithms
Page 41 and 42: Don’t Mess With It! Anyone Else K
Page 43 and 44: Page 43/65 CRISP - DM � CRoss-Ind
Page 45 and 46: Mapping generic models to specializ
Page 47 and 48: � Business understanding � Data
Page 49 and 50: Data Understanding � Collect init
Page 51 and 52: Page 51/65 Modeling � Select mode
Page 53 and 54: � Plan deployment Page 53/65 Depl
Page 55 and 56: Available Software Page 55/65 14
Page 57 and 58:
Criteria for evaluating DM tools A
Page 59 and 60:
Criteria for evaluating DM tools
Page 61 and 62:
Evaluation of forteen DM tools Page
Page 63 and 64:
Page 63/65 WWW.NBA.COM
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� CD - ROM � Page 65/65
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Data Mining Versus Semantic Web

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