47,982 research outputs found
A bi-criteria approximation algorithm for Means
We consider the classical -means clustering problem in the setting
bi-criteria approximation, in which an algoithm is allowed to output clusters, and must produce a clustering with cost at most times the
to the cost of the optimal set of clusters. We argue that this approach is
natural in many settings, for which the exact number of clusters is a priori
unknown, or unimportant up to a constant factor. We give new bi-criteria
approximation algorithms, based on linear programming and local search,
respectively, which attain a guarantee depending on the number
of clusters that may be opened. Our gurantee is
always at most and improves rapidly with (for example:
, and ). Moreover, our algorithms have only
polynomial dependence on the dimension of the input data, and so are applicable
in high-dimensional settings
Robust Correlation Clustering
In this paper, we introduce and study the Robust-Correlation-Clustering problem: given a graph G = (V,E) where every edge is either labeled + or - (denoting similar or dissimilar pairs of vertices), and a parameter m, the goal is to delete a set D of m vertices, and partition the remaining vertices V D into clusters to minimize the cost of the clustering, which is the sum of the number of + edges with end-points in different clusters and the number of - edges with end-points in the same cluster. This generalizes the classical Correlation-Clustering problem which is the special case when m = 0. Correlation clustering is useful when we have (only) qualitative information about the similarity or dissimilarity of pairs of points, and Robust-Correlation-Clustering equips this model with the capability to handle noise in datasets.
In this work, we present a constant-factor bi-criteria algorithm for Robust-Correlation-Clustering on complete graphs (where our solution is O(1)-approximate w.r.t the cost while however discarding O(1) m points as outliers), and also complement this by showing that no finite approximation is possible if we do not violate the outlier budget. Our algorithm is very simple in that it first does a simple LP-based pre-processing to delete O(m) vertices, and subsequently runs a particular Correlation-Clustering algorithm ACNAlg [Ailon et al., 2005] on the residual instance. We then consider general graphs, and show (O(log n), O(log^2 n)) bi-criteria algorithms while also showing a hardness of alpha_MC on both the cost and the outlier violation, where alpha_MC is the lower bound for the Minimum-Multicut problem
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