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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 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 Sample Clustering.

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

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