TITLE: Hierarchical clustering: Objective functions and new algorithms
SPEAKER: Varun Kanade (Department of Computer Science, University of Oxford)

ABSTRACT:
Hierarchical clustering is a recursive partitioning of a dataset into
clusters at an increasingly finer granularity. Hierarchical clustering
has mostly been studied in procedural terms, i.e., a hierarchical
cluster tree is obtained using certain bottom-up (agglomerative) or
top-down (divisive) heuristics, rather than finding a hierarchical
cluster tree as the solution obtained by optimizing some suitable
objective. Dasgupta (2016) identified this as a reason why the theory of
hierarchical clustering lagged behind that of flat clustering and
proposed an objective function. In this talk, I will take an axiomatic
approach to identifying suitable objective functions for hierarchical
clustering. I will also describe a new random-graph model with a planted
hierarchy. New and existing algorithms and their performance in theory
and on some preliminary experiments will be discussed. 

Based on joint work with Vincent Cohen-Addad, Claire Mathieu and
Frederik Mallmann-Trenn.