The problem of constrained clustering has attracted significant attention in the past decades. In this paper, we study the balanced @math -center, @math -median, and @math -means clustering problems where the size of each cluster is constrained by the given lower and upper bounds. The problems are motivated by the applications in processing large-scale data in high dimension. Existing methods often need to compute complicated matchings (or min cost flows) to satisfy the balance constraint, and thus suffer from high complexities especially in high dimension. We develop an effective framework for the three balanced clustering problems to address this issue, and our method is based on a novel spatial partition idea in geometry. For the balanced @math -center clustering, we provide a @math -approximation algorithm that improves the existing approximation factors; for the balanced @math -median and @math -means clusterings, our algorithms yield constant and @math -approximation factors with any @math . More importantly, our algorithms achieve linear or nearly linear running times when @math is a constant, and significantly improve the existing ones. Our results can be easily extended to metric balanced clusterings and the running times are sub-linear in terms of the complexity of @math -point metric.
@cite_24: Given a set of points P ⊂ Rd and value ∊ > 0, an ∊-core-set S ⊂ P has the property that the smallest ball containing S is an ∊-approximation of the smallest ball containing P. This paper shows that any point-set has an ∊-core-set of size [2 ∊]. We also give a fast algorithm that finds this core-set. These results imply the existence of small core-sets for solving approximate k-center clustering and related problems. The sizes of these core-sets are considerably smaller than the previously known bounds, and imply faster algorithms; one such algorithm needs O(dn ∊ + (l ∊)5) time to compute an ∊-approximate minimum enclosing ball (1-center) of n points in d dimensions. A simple gradient-descent algorithm is also given, for computing the minimum enclosing ball in O(dn ∊2) time. This algorithm also implies slightly faster algorithms for computing approximately the smallest radius k-flat fitting a set of points.
@cite_53: In this paper, we show that for several clustering problems one can extract a small set of points, so that using those core-sets enable us to perform approximate clustering efficiently. The surprising property of those core-sets is that their size is independent of the dimension.Using those, we present a (1+ e)-approximation algorithms for the k-center clustering and k-median clustering problems in Euclidean space. The running time of the new algorithms has linear or near linear dependency on the number of points and the dimension, and exponential dependency on 1 e and k. As such, our results are a substantial improvement over what was previously known.We also present some other clustering results including (1+ e)-approximate 1-cylinder clustering, and k-center clustering with outliers.
@cite_22: The problem of clustering a set of points so as to minimize the maximum intercluster distance is studied. An O(kn) approximation algorithm, where n is the number of points and k is the number of clusters, that guarantees solutions with an objective function value within two times the optimal solution value is presented. This approximation algorithm succeeds as long as the set of points satisfies the triangular inequality. We also show that our approximation algorithm is best possible, with respect to the approximation bound, if PZ NP.
@cite_33: In this paper we present a 2-approximation algorithm for the k-center problem with triangle inequality. This result is “best possible” since for any δ < 2 the existence of δ-approximation algorithm would imply that P = NP. It should be noted that no δ-approximation algorithm, for any constant δ, has been reported to date. Linear programming duality theory provides interesting insight to the problem and enables us to derive, in O|E| log |E| time, a solution with value no more than twice the k-center optimal value. A by-product of the analysis is an O|E| algorithm that identifies a dominating set in G2, the square of a graph G, the size of which is no larger than the size of the minimum dominating set in the graph G. The key combinatorial object used is called a strong stable set, and we prove the NP-completeness of the corresponding decision problem.
The optimal approximation results of the ordinary @math -center clustering appeared in the 80's: Gonazlez @cite_22 and Hochbaum and Shmoys @cite_33 respectively provided a 2-approximation and proved that any approximation ratio @math would imply @math . If @math is fixed, we have the PTAS (Polynomial Time Approximation Scheme) for @math -center clustering through constructing the core-set of minimum enclosing ball @cite_53 @cite_24 .