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LFCS seminar: Varun Kanade: Hierarchical clustering: Objective functions and efficient algorithms

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What
  • LFCS Seminar
  • Upcoming events
When May 08, 2018
from 04:00 PM to 05:00 PM
Where IF 4.31/4.33
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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.

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