The diameter k-clustering problem is the problem of partitioning a finite subset of ℝ d into k subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem (for all values of k) is the agglomerative clustering algorithm with the complete linkage strategy. For decades, this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper, we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension d is a constant, we show that for any k the solution computed by this algorithm is an O(logk)-approximation to the diameter k-clustering problem. Our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm. Furthermore, we analyze the closely related k-center and discrete k-center problem. For the corresponding agglomerative algorithms, we deduce an approximation factor of O(logk) as well.
For the Euclidean case, we know that for fixed @math , i.e. when we are not interested in a hierarchy of clusterings, the diameter @math -clustering problem and the @math -center problem are @math -hard. In fact, it is already @math -hard to approximate both problems with an approximation factor below @math and @math respectively @cite_11 .
@cite_11
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In a clustering problem, the aim is to partition a given set of n points in d -dimensional space into k groups, called clusters, so that points within each cluster are near each other. Two objective functions frequently used to measure the performance of a clustering algorithm are, for any L 4 metric, (a) the maximum distance between pairs of points in the same cluster, and (b) the maximum distance between points in each cluster and a chosen cluster center; we refer to either measure as the cluster size. We show that one cannot approximate the optimal cluster size for a fixed number of clusters within a factor close to 2 in polynomial time, for two or more dimensions, unless P=NP. We also present an algorithm that achieves this factor of 2 in time O ( n log k ), and show that this running time is optimal in the algebraic decision tree model. For a fixed cluster size, on the other hand, we give a polynomial time approximation scheme that estimates the optimal number of clusters under the second measure of cluster size within factors arbitrarily close to 1. Our approach is extended to provide approximation algorithms for the restricted centers, suppliers, and weighted suppliers problems that run in optimal O ( n log k ) time and achieve optimal or nearly optimal approximation bounds.