Hierarchical Clustering: A 0.585 Revenue Approximation

Noga Alon, Yossi Azar, Dan Vainstein

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


Hierarchical Clustering trees have been widely accepted as a useful form of clustering data, resulting in a prevalence of adopting fields including phylogenetics, image analysis, bioinformatics and more. Recently, Dasgupta (STOC 16’) initiated the analysis of these types of algorithms through the lenses of approximation. Later, the dual problem was considered by Moseley and Wang (NIPS 17’) dubbing it the Revenue goal function. In this problem, given a nonnegative weight wij for each pair i, j ∈ [n] = {1, 2, . . . , n}, the objective is to find a tree T whose set of leaves is [n] that maximizes the function P
i<j∈[n] wij (n − |Tij |), where |Tij | is the number of leaves in the subtree
rooted at the least common ancestor of i and j.
In our work we consider the revenue goal function and prove the following results. First, we prove the existence of a bisection (i.e., a tree of depth 2 in which the root has two children, each being a parent of n/2 leaves) which approximates the general optimal tree solution up to a factor of
1/2 (which is tight). Second, we apply this result in order to prove a 2/3p approximation for the general revenue problem, where p is defined as the approximation ratio of the MAX-UNCUT BISECTION problem. Since p is known to be at least 0.8776 (Austrin et al., 2016) (Wu et al., 2015), we get a 0.585 approximation algorithm for the revenue problem. This improves a sequence of earlier results which culminated in an 0.4246-approximation guarantee (Ahmadian et al., 2019).
اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفProceedings of Machine Learning Research
العنوان الفرعي لمنشور المضيفProceedings of Thirty Third Conference on Learning Theory, PMLR
المحررونJacob Abernethy, Shivani Agarwal
عدد الصفحات10
مستوى الصوت125
حالة النشرنُشِر - 2020
الحدثConference on Learning Theory, COLT 2020 - Graz, النمسا
المدة: ٩ يوليو ٢٠٢٠١٢ يوليو ٢٠٢٠


!!ConferenceConference on Learning Theory, COLT 2020
المسمى المختصرcolt2020
عنوان الإنترنت


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