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Bayesian Hierarchic Sample Clustering
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
En bayesiansk metod för hierarkisk klassificering (Swedish)
Abstract [en]

This report presents a novel algorithm for hierarchical clustering called Bayesian Sample Clustering (BSC). BSC is a single linkage algorithm that uses data samples to produce a predictive distribution for each sample. The predictive distributions are compared using the Chan-Darwiche distance, a metric for finite probability distributions, to produce a hierarchy of samples. The implemented version of BSC is found at https://github.com/Skjulet/Bayesian Sample Clustering.

 

Abstract [sv]

Denna rapport presenterar en ny algoritm för hierarkisk klustring, Bayesian Sample Clustering (BSC). BSC är en single-linkage algoritm som använder stickprov av data för att skapa en prediktiv fördelning för varje stickprov. De prediktiva fördelningarna jämförs med Chan-Darwiche avståndet, en metrik över ändliga sannolikhetsfördelningar, vilket möjliggör skapandet av en hierarki av kluster. BSC finns i implementerad version på https://github.com/Skjulet/Bayesian Sample Clustering.

Place, publisher, year, edition, pages
2015.
Series
TRITA-MAT-E, 2015:26
National Category
Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-168316OAI: oai:DiVA.org:kth-168316DiVA: diva2:818932
Subject / course
Mathematics
Educational program
Master of Science - Mathematics
Supervisors
Examiners
Available from: 2015-06-09 Created: 2015-06-01 Last updated: 2015-06-09Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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Language
  • de-DE
  • en-GB
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  • Other locale
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Output format
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