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A data-splitting approach for comparing hierarchical clustering algorithms

Grant number: 20/10861-7
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): November 01, 2020
Effective date (End): October 31, 2021
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal Investigator:Rafael Izbicki
Grantee:Luben Miguel Cruz Cabezas
Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil


Clustering methods have the goal of grouping together sample points that have similar properties. Several clustering techniques have been developed. An important question in practice is how to choose which method to use. While several approaches to answer this question have been proposed, they do not focus on hierarchical clustering methods because these do not yield a single partition of the observations. Thus, these evaluation techniques do not make use of the richer structure provided by these methods. In this project, we propose an alternative framework to compare clustering algorithms that is especially tailored for hierarchical methods. Our approach is also based on evaluating the predictive power of a given algorithm by using tools from phylogenetic analysis.

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