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On Dasgupta’s Hierarchical Clustering Objective and Its Relation to Other Graph Parameters

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Fundamentals of Computation Theory (FCT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12867))

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Abstract

The minimum height of vertex and edge partition trees are well-studied graph parameters known as, for instance, vertex and edge ranking number. While they are NP-hard to determine in general, linear-time algorithms exist for trees. Motivated by a correspondence with Dasgupta’s objective for hierarchical clustering we consider the total rather than maximum depth of vertices as an alternative objective for minimization. For vertex partition trees this leads to a new parameter with a natural interpretation as a measure of robustness against vertex removal.

As tools for the study of this family of parameters we show that they have similar recursive expressions and prove a binary tree rotation lemma. The new parameter is related to trivially perfect graph completion and therefore intractable like the other three are known to be. We give polynomial-time algorithms for both total-depth variants on caterpillars and on trees with a bounded number of leaf neighbors. For general trees, we obtain a 2-approximation algorithm.

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Correspondence to Svein Høgemo .

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Høgemo, S., Bergougnoux, B., Brandes, U., Paul, C., Telle, J.A. (2021). On Dasgupta’s Hierarchical Clustering Objective and Its Relation to Other Graph Parameters. In: Bampis, E., Pagourtzis, A. (eds) Fundamentals of Computation Theory. FCT 2021. Lecture Notes in Computer Science(), vol 12867. Springer, Cham. https://doi.org/10.1007/978-3-030-86593-1_20

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  • DOI: https://doi.org/10.1007/978-3-030-86593-1_20

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