Small space representations for metric min-sum k-clustering and their applications
Czumaj, Artur and Sohler, Christian (2007) Small space representations for metric min-sum k-clustering and their applications. In: Thomas, W. and Weil, P., (eds.) STACS 2007 : 24th Annual Symposium on Theoretical Aspects of Computer Science, Aachen, Germany, February 22-24, 2007. Proceedings. Lecture Notes in Computer Science, Volume 4393 . Springer Berlin Heidelberg, pp. 536-548. ISBN 9783540709176Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/978-3-540-70918-3_46
The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C-1, . . . , C-k subset of P such that Sigma(k)(i=1), Sigma(p,q is an element of Ci) d(p, q) is minimized. We show the first efficient construction of a coreset for this problem. Our coreset construction is based on a new adaptive sampling algorithm. Using our coresets we obtain three main algorithmic results.
The first result is a sublinear time (4 + is an element of)-approximation algorithm for the min-sum k-clustering problem in metric spaces. The running time of this algorithm is (O) over tilde (n) for any constant k and E, and it is o(n(2)) for all k = o(log n/ log log n). Since the description size of the input is Theta(n(2)), this is sublinear in the input size.
Our second result is the first pass-efficient data streaming algorithm for min-sum k-clustering in the distance oracle model, i.e., an algorithm that uses poly (log n, k) space and makes 2 passes over the input point set arriving as a data stream.
Our third result is a sublinear-time polylogarithmic-factor-approximation algorithm for the min-sum k-clustering problem for arbitrary values of k.
To develop the coresets, we introduce the concept of alpha-preserving metric embeddings. Such an embedding satisfies properties that (a) the distance between any pair of points does not decrease, and (b) the cost of an optimal solution for the considered problem on input (P, d') is within a constant factor of the optimal solution on input (P, d). In other words, the idea is find a metric embedding into a (structurally simpler) metric space that approximates the original metric up to a factor of a with respect to a certain problem. We believe that this concept is an interesting generalization of coresets.
|Item Type:||Book Item|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software|
|Divisions:||Faculty of Science > Computer Science|
|Series Name:||Lecture Notes in Computer Science|
|Journal or Publication Title:||Stacs 2007, Proceedings|
|Publisher:||Springer Berlin Heidelberg|
|Book Title:||STACS 2007 : 24th Annual Symposium on Theoretical Aspects of Computer Science, Aachen, Germany, February 22-24, 2007. Proceedings|
|Editor:||Thomas, W. and Weil, P.|
|Number of Pages:||13|
|Page Range:||pp. 536-548|
|Access rights to Published version:||Restricted or Subscription Access|
|Title of Event:||24th Annual Symposium on Theoretical Aspects of Computer Science|
|Location of Event:||Aachen, Germany|
|Date(s) of Event:||22-24 Feb 2007|
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