We initiate the study of coresets for clustering in graph metrics, i.e., the shortest-path metric of edge-weighted graphs. Such clustering problems (on graph metrics) are essential to data analysis and used for example in road networks and data visualization. Specifically, we consider @math -Clustering, where given a metric space @math , the goal is to minimize, over all @math -point center sets @math , the objective @math . This problem is a well-known generalization of both k-Median ( @math ) and k-Means ( @math ). A coreset is a compact summary of the data that approximately preserves the clustering objective for every possible center set. Coresets offer significant efficiency improvements in terms of running time, storage, and communication, including in streaming and distributed settings. Our main result is a near-linear time construction of a coreset of size @math for @math -Clustering in a graph @math whose treewidth is @math . The construction is based on the framework of Feldman and Langberg [STOC 2011], and our main technical contribution, as required by this framework, is a uniform bound of @math on the shattering dimension under any point weights. Previously, the only construction applicable to graph metrics, even for @math , was a generic one with size @math where @math [Feldman and Langberg, STOC 2011]. We complement our construction with an @math size lower bound, which matches our construction's linear dependence on @math . This further provides the first proof that the @math factor in the generic upper bound is indeed necessary, and also justifies restricting the graph topology.
We design a data stream algorithm for the k-means problem, called BICO, that combines the data structure of the SIGMOD Test of Time award winning algorithm BIRCH [27] with the theoretical concept of coresets for clustering problems. The k-means problem asks for a set C of k centers minimizing the sum of the squared distances from every point in a set P to its nearest center in C. In a data stream, the points arrive one by one in arbitrary order and there is limited storage space.  Abstract Every graph generated by a hyperedge replacement graph-grammar can be represented by a tree, namely the derivation tree of the derivation sequence that produced it. Certain functions on graphs can be computed recursively on the derivation trees of these graphs. By using monadic second-order logic and semiring homomorphisms, we describe in a single formalism a large class of such functions. Polynomial and even linear algorithms can be constructed for some of these functions. We unify similar results obtained by Takamizawa (1982), Bern (1987), Arnborg (1991) and Habel (1989). Abstract A general problem in computational graph theory is that of finding an optimal subgraph H of a given weighted graph G . The matching problem (which is easy) and the traveling salesman problem (which is not) are well-known examples of this general problem. In the literature one can also find a variety of ad hoc algorithms for solving certain special cases in linear time. We suggest a general approach for constructing linear-time algorithms in the case where the graph G is defined by certain rules of composition (as are trees, series-parallel graphs, and outerplanar graphs) and the desired subgraph H satisfies a property that is “regular” with respect to these rules of composition (as do matchings, dominating sets, and independent sets for all the classes just mentioned). This approach is applied to obtain a linear-time algorithm for computing the irredundance number of a tree, a problem for which no polynomial-time algorithm was previously known. @d can be approximated up to (1 + e)-factor, for an arbitrary small e > 0, using the O(k e2)-rank approximation of A and a constant. This implies, for example, that the optimal k-means clustering of the rows of A is (1 + e)-approximated by an optimal k-means clustering of their projection on the O(k e2) first right singular vectors (principle components) of A. A (j, k)-coreset for projective clustering is a small set of points that yields a (1 + e)-approximation to the sum of squared distances from the n rows of A to any set of k affine subspaces, each of dimension at most j. Our embedding yields (0, k)-coresets of size O(k) for handling k-means queries, (j, 1)-coresets of size O(j) for PCA queries, and (j, k)-coresets of size (log n)O(jk) for any j, k ≥ 1 and constant e e (0, 1 2). Previous coresets usually have a size which is linearly or even exponentially dependent of d, which makes them useless when d n. Using our coresets with the merge-and-reduce approach, we obtain embarrassingly parallel streaming algorithms for problems such as k-means, PCA and projective clustering. These algorithms use update time per point and memory that is polynomial in log n and only linear in d. For cost functions other than squared Euclidean distances we suggest a simple recursive coreset construction that produces coresets of size In this paper, we show the existence of small coresets for the problems of computing k-median and k-means clustering for points in low dimension. In other words, we show that given a point set P in Rd, one can compute a weighted set S ⊆ P, of size O(k e-d log n), such that one can compute the k-median means clustering on S instead of on P, and get an (1+e)-approximation. As a result, we improve the fastest known algorithms for (1+e)-approximate k-means and k-median. Our algorithms have linear running time for a fixed k and e. In addition, we can maintain the (1+e)-approximate k-median or k-means clustering of a stream when points are being only inserted, using polylogarithmic space and update time. This paper gives an overview of several results and techniques for graphs algorithms that compute the treewidth of a graph or that solve otherwise intractable problems when restricted graphs with bounded treewidth more efficiently. Also, several results on graph minors are reviewed.  Abstract We present and illustrate by a sequence of examples an algorithm paradigm for solving NP- hard problems on graphs restricted to partial graphs of k -trees and given with an embedding in a k -tree. Such algorithms, linear in the size of the graph but exponential or superexponential in k , exist for most NP-hard problems that have linear time algorithms for trees. The examples used are optimization problems involving independent sets, dominating sets, graph coloring, Hamiltonian circuits, network reliability and minimum vertex deletion forbidden subgraphs. The results generalize previous results for series-parallel graphs, bandwidth-constrained graphs, and non- serial dynamic programming. In this paper we present an n^ O(k1-1 d) -time algorithm for solving the k -center problem in , under Lźfty - and L2 -metrics. The algorithm extends to other metrics, and to the discrete k -center problem. We also describe a simple (1+ź) -approximation algorithm for the k -center problem, with running time O(nlog k) + (k ź)^ O(k1-1 d) . Finally, we present an n^ O(k1-1 d) -time algorithm for solving the L -capacitated k -center problem, provided that L=Ω(n k1-1 d) or L=O(1) . We present new approximation algorithms for the @math -median and @math -means clustering problems. To this end, we obtain small coresets for @math -median and @math -means clustering in general metric spaces and in Euclidean spaces. In @math , these coresets are of size with polynomial dependency on the dimension @math . This leads to @math -approximation algorithms to the optimal @math -median and @math -means clustering in @math , with running time @math , where @math is the number of points. This improves over previous results. We use those coresets to maintain a @math -approximate @math -median and @math -means clustering of a stream of points in @math , using @math space. These are the first streaming algorithms, for those problems, that have space complexity with polynomial dependency on the dimension.