Faster balanced clusterings in high dimension
نویسندگان
چکیده
منابع مشابه
Local Search for Balanced Submodular Clusterings
In this paper, we consider the problem of producing balanced clusterings with respect to a submodular objective function. Submodular objective functions occur frequently in many applications, and hence this problem is broadly applicable. We show that the results of Patkar and Narayanan [8] can be applied to cases when the submodular function is derived from a bipartite object-feature graph, and...
متن کاملClustering en haute dimension par accumulation de clusterings locaux
Résumé. Le clustering est une tâche fondamentale de la fouille de données. Ces dernières années, les méthodes de type cluster ensembles ont été l’objet d’une attention soutenue. Il s’agit d’agréger plusieurs clusterings d’un jeu de données afin d’obtenir un clustering "moyen". Les clusterings individuels peuvent être le résultat de différents algorithmes. Ces méthodes sont particulièrement util...
متن کاملComplex Dimension Faster and Deterministically ( Extended
We give a new complexity bound for calculating the complex dimension of an algebraic set. Our algorithm is completely deterministic and approaches the best recent randomized complexity bounds. We also present some new, significantly sharper quantitative estimates on rational univariate representations (RUR) of roots of polynomial systems. As a corollary of the latter bounds, we considerably imp...
متن کاملPairwise balanced designs of dimension three
The dimension of a pairwise balanced design PBD(v,K) is the maximum positive integer d such that any d of its points are contained in a proper flat. The standard examples are affine and projective linear spaces. Also, Teirlinck provided a nearly complete existence theory for ‘Steiner spaces’, which is the case K = {3} and d = 3. A recent result of the first author and A.C.H. Ling says that ther...
متن کاملFrom Comparing Clusterings to Combining Clusterings
This paper presents a fast simulated annealing framework for combining multiple clusterings (i.e. clustering ensemble) based on some measures of agreement between partitions, which are originally used to compare two clusterings (the obtained clustering vs. a ground truth clustering) for the evaluation of a clustering algorithm. Though we can follow a greedy strategy to optimize these measures a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Theoretical Computer Science
سال: 2020
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2020.07.022