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You searched for subject:(interestingness measures). Showing records 1 – 8 of 8 total matches.

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Kansas State University

1. Aljandal, Waleed A. Itemset size-sensitive interestingness measures for association rule mining and link prediction.

Degree: PhD, Department of Computing and Information Sciences, 2009, Kansas State University

 Association rule learning is a data mining technique that can capture relationships between pairs of entities in different domains. The goal of this research is… (more)

Subjects/Keywords: Data Mining; Association Rule; Interestingness Measures; Link Prediction; Computer Science (0984)

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APA (6th Edition):

Aljandal, W. A. (2009). Itemset size-sensitive interestingness measures for association rule mining and link prediction. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/1245

Chicago Manual of Style (16th Edition):

Aljandal, Waleed A. “Itemset size-sensitive interestingness measures for association rule mining and link prediction.” 2009. Doctoral Dissertation, Kansas State University. Accessed January 29, 2020. http://hdl.handle.net/2097/1245.

MLA Handbook (7th Edition):

Aljandal, Waleed A. “Itemset size-sensitive interestingness measures for association rule mining and link prediction.” 2009. Web. 29 Jan 2020.

Vancouver:

Aljandal WA. Itemset size-sensitive interestingness measures for association rule mining and link prediction. [Internet] [Doctoral dissertation]. Kansas State University; 2009. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/2097/1245.

Council of Science Editors:

Aljandal WA. Itemset size-sensitive interestingness measures for association rule mining and link prediction. [Doctoral Dissertation]. Kansas State University; 2009. Available from: http://hdl.handle.net/2097/1245


Mississippi State University

2. Manda, Prashanti. Novel algorithms for cross-ontology multi-level data mining.

Degree: PhD, Computer Science and Engineering, 2012, Mississippi State University

  The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining… (more)

Subjects/Keywords: association rule mining; cross-ontology data mining; interestingness measures; gene ontology; anatomy ontology

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APA (6th Edition):

Manda, P. (2012). Novel algorithms for cross-ontology multi-level data mining. (Doctoral Dissertation). Mississippi State University. Retrieved from http://sun.library.msstate.edu/ETD-db/theses/available/etd-10292012-125818/ ;

Chicago Manual of Style (16th Edition):

Manda, Prashanti. “Novel algorithms for cross-ontology multi-level data mining.” 2012. Doctoral Dissertation, Mississippi State University. Accessed January 29, 2020. http://sun.library.msstate.edu/ETD-db/theses/available/etd-10292012-125818/ ;.

MLA Handbook (7th Edition):

Manda, Prashanti. “Novel algorithms for cross-ontology multi-level data mining.” 2012. Web. 29 Jan 2020.

Vancouver:

Manda P. Novel algorithms for cross-ontology multi-level data mining. [Internet] [Doctoral dissertation]. Mississippi State University; 2012. [cited 2020 Jan 29]. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10292012-125818/ ;.

Council of Science Editors:

Manda P. Novel algorithms for cross-ontology multi-level data mining. [Doctoral Dissertation]. Mississippi State University; 2012. Available from: http://sun.library.msstate.edu/ETD-db/theses/available/etd-10292012-125818/ ;


Queensland University of Technology

3. Shaw, Gavin. Discovery & effective use of quality association rules in multi-level datasets.

Degree: 2010, Queensland University of Technology

 In today’s electronic world vast amounts of knowledge is stored within many datasets and databases. Often the default format of this data means that the… (more)

Subjects/Keywords: association rules, multi-level association rules, multi-level datasets, redundancy, non-redundant association rules, interestingness measures, diversity measure, distance measure; recommender systems, cold-start problem, user profile expansion

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APA (6th Edition):

Shaw, G. (2010). Discovery & effective use of quality association rules in multi-level datasets. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/41731/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Shaw, Gavin. “Discovery & effective use of quality association rules in multi-level datasets.” 2010. Thesis, Queensland University of Technology. Accessed January 29, 2020. https://eprints.qut.edu.au/41731/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Shaw, Gavin. “Discovery & effective use of quality association rules in multi-level datasets.” 2010. Web. 29 Jan 2020.

Vancouver:

Shaw G. Discovery & effective use of quality association rules in multi-level datasets. [Internet] [Thesis]. Queensland University of Technology; 2010. [cited 2020 Jan 29]. Available from: https://eprints.qut.edu.au/41731/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Shaw G. Discovery & effective use of quality association rules in multi-level datasets. [Thesis]. Queensland University of Technology; 2010. Available from: https://eprints.qut.edu.au/41731/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Grissa, Dhouha. Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction.

Degree: Docteur es, Informatique, 2013, Université Blaise-Pascale, Clermont-Ferrand II

 La recherche de règles d’association intéressantes est un domaine important et actif en fouille de données. Puisque les algorithmes utilisés en extraction de connaissances à… (more)

Subjects/Keywords: Extraction de Connaissances à partir des Données (ECD); Mesures d’intérêt; Propriétés formelles; Règles d’association; Classification non supervisée; Analyse factorielle booléenne; Knowledge Discovery from Databases (KDD); Interestingness measures; Formal properties; Association rules; Clustering; Boolean factor analysis

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APA (6th Edition):

Grissa, D. (2013). Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2013CLF22401

Chicago Manual of Style (16th Edition):

Grissa, Dhouha. “Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction.” 2013. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed January 29, 2020. http://www.theses.fr/2013CLF22401.

MLA Handbook (7th Edition):

Grissa, Dhouha. “Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction.” 2013. Web. 29 Jan 2020.

Vancouver:

Grissa D. Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2013. [cited 2020 Jan 29]. Available from: http://www.theses.fr/2013CLF22401.

Council of Science Editors:

Grissa D. Etude comportementale des mesures d'intérêt d'extraction de connaissances : Behavioral study of interestingness measures of knowledge extraction. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2013. Available from: http://www.theses.fr/2013CLF22401

5. Desmier, Elise. Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués.

Degree: Docteur es, Informatique, 2014, INSA Lyon

Cette thèse s'est déroulée dans le cadre du projet ANR FOSTER, "FOuille de données Spatio-Temporelles : application à la compréhension et à la surveillance de… (more)

Subjects/Keywords: Informatique; Fouille de données; Fouille sous contrainte; Données spatio-Temporelles; Graphes dynamiques attribués; Motifs de co-Evolution; Mesures d'intérêt; Analyse skyline; Information Technology; Data mining; Constraint-Based mining; Spatio-Temporal data; Dynamic attributed graphs; Co-Evolution patterns; Interestingness measures; Skyline analysis; 006.310 72

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Desmier, E. (2014). Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués. (Doctoral Dissertation). INSA Lyon. Retrieved from http://www.theses.fr/2014ISAL0071

Chicago Manual of Style (16th Edition):

Desmier, Elise. “Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués.” 2014. Doctoral Dissertation, INSA Lyon. Accessed January 29, 2020. http://www.theses.fr/2014ISAL0071.

MLA Handbook (7th Edition):

Desmier, Elise. “Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués.” 2014. Web. 29 Jan 2020.

Vancouver:

Desmier E. Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués. [Internet] [Doctoral dissertation]. INSA Lyon; 2014. [cited 2020 Jan 29]. Available from: http://www.theses.fr/2014ISAL0071.

Council of Science Editors:

Desmier E. Co-evolution pattern mining in dynamic attributed graphs : Fouille de motifs de co-evolution dans des graphes dynamiques attribués. [Doctoral Dissertation]. INSA Lyon; 2014. Available from: http://www.theses.fr/2014ISAL0071


University of Waterloo

6. Li, Chung Lam. Association Pattern Analysis for Pattern Pruning, Clustering and Summarization.

Degree: 2008, University of Waterloo

 Automatic pattern mining from databases and the analysis of the discovered patterns for useful information are important and in great demand in science, engineering and… (more)

Subjects/Keywords: Pattern clustering; Pattern pruning; Pattern summarization; Pattern discovery; Simultaneous pattern and data clustering; Pattern post-analysis; Interestingness measures; Association rules; Itemsets

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Li, C. L. (2008). Association Pattern Analysis for Pattern Pruning, Clustering and Summarization. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/4022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Li, Chung Lam. “Association Pattern Analysis for Pattern Pruning, Clustering and Summarization.” 2008. Thesis, University of Waterloo. Accessed January 29, 2020. http://hdl.handle.net/10012/4022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Li, Chung Lam. “Association Pattern Analysis for Pattern Pruning, Clustering and Summarization.” 2008. Web. 29 Jan 2020.

Vancouver:

Li CL. Association Pattern Analysis for Pattern Pruning, Clustering and Summarization. [Internet] [Thesis]. University of Waterloo; 2008. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10012/4022.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Li CL. Association Pattern Analysis for Pattern Pruning, Clustering and Summarization. [Thesis]. University of Waterloo; 2008. Available from: http://hdl.handle.net/10012/4022

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

7. Hrovat, Goran. Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih.

Degree: 2018, Univerza v Mariboru

Zanimivosti asociativnih pravil ali pogostih množic postavk se lahko skozi čas spreminjajo. Prav tako je lahko njihova zanimivost različna za različne skupine (npr. skupine ljudi).… (more)

Subjects/Keywords: podatkovno rudarjenje; mere zanimivosti; asociativna pravila; podpora odločanju; regresijsko dre-vo; linearna regresija; elektronski zdravstveni zapis; data mining; interestingness measures; association rules; decision support; regression tree; linear regression; electronic health record; info:eu-repo/classification/udc/005.31:519.816(043.3)

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APA (6th Edition):

Hrovat, G. (2018). Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih. (Doctoral Dissertation). Univerza v Mariboru. Retrieved from https://dk.um.si/IzpisGradiva.php?id=70954 ; https://dk.um.si/Dokument.php?id=126337&dn= ; https://plus.si.cobiss.net/opac7/bib/21567254?lang=sl

Chicago Manual of Style (16th Edition):

Hrovat, Goran. “Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih.” 2018. Doctoral Dissertation, Univerza v Mariboru. Accessed January 29, 2020. https://dk.um.si/IzpisGradiva.php?id=70954 ; https://dk.um.si/Dokument.php?id=126337&dn= ; https://plus.si.cobiss.net/opac7/bib/21567254?lang=sl.

MLA Handbook (7th Edition):

Hrovat, Goran. “Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih.” 2018. Web. 29 Jan 2020.

Vancouver:

Hrovat G. Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih. [Internet] [Doctoral dissertation]. Univerza v Mariboru; 2018. [cited 2020 Jan 29]. Available from: https://dk.um.si/IzpisGradiva.php?id=70954 ; https://dk.um.si/Dokument.php?id=126337&dn= ; https://plus.si.cobiss.net/opac7/bib/21567254?lang=sl.

Council of Science Editors:

Hrovat G. Rekurzivna delitev modelov linearne regresije za oceno zanimivosti asociativnih pravil v različnih časovnih obdobjih. [Doctoral Dissertation]. Univerza v Mariboru; 2018. Available from: https://dk.um.si/IzpisGradiva.php?id=70954 ; https://dk.um.si/Dokument.php?id=126337&dn= ; https://plus.si.cobiss.net/opac7/bib/21567254?lang=sl

8. Bouker, Slim. Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences.

Degree: Docteur es, Informatique, 2015, Université Blaise-Pascale, Clermont-Ferrand II

Résumé indisponible.

Résumé indisponible.

Advisors/Committee Members: Mephu-Nguifo, Engelbert (thesis director), Ben Yahia, Sadok (thesis director).

Subjects/Keywords: Fouille de données; Extraction des règles d'association; Mesures de qualité; Préférences des experts; Relation de dominance; Data mining; Extraction of association rules; Interestingness measures; Experts preferences; Dominance relationship

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APA (6th Edition):

Bouker, S. (2015). Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2015CLF22585

Chicago Manual of Style (16th Edition):

Bouker, Slim. “Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences.” 2015. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed January 29, 2020. http://www.theses.fr/2015CLF22585.

MLA Handbook (7th Edition):

Bouker, Slim. “Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences.” 2015. Web. 29 Jan 2020.

Vancouver:

Bouker S. Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. [cited 2020 Jan 29]. Available from: http://www.theses.fr/2015CLF22585.

Council of Science Editors:

Bouker S. Contribution à l'extraction des règles d'association basée sur des préférences : Contribution to the extraction of association rules based on preferences. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. Available from: http://www.theses.fr/2015CLF22585

.