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- Marcel R. Ackermann, Marcus Märtens, +4 authors Christian Sohler
- 2012

We develop a new k-means clustering algorithm for data streams of points from a Euclidean space. We call this algorithm StreamKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the k-means++ algorithm of Arthur and Vassilvitskii (SODA '07). To compute the small sample, we propose two new… (More)

We develop a new <it>k</it>-means clustering algorithm for data streams of points from a Euclidean space. We call this algorithm StreamKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the <it>k</it>-means++ algorithm of Arthur and Vassilvitskii (SODA '07). To compute… (More)

- Joachim Gehweiler, Christiane Lammersen, Christian Sohler
- Algorithmica
- 2006

In this paper, we present a randomized constant factor approximation algorithm for the metric minimum facility location problem with uniform costs and demands in a distributed setting, in which every point can open a facility. In particular, our distributed algorithm uses three communication rounds with message sizes bounded to <i>O</i>(log <i>n</i>) bits… (More)

We develop a new k-means clustering algorithm for data streams, which we call StreamKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the k-means++ algorithm [1]. To compute the small sample, we propose two new techniques. First, we use a non-uniform sampling approach similar to the k-means++… (More)

- Christiane Lammersen, Christian Sohler
- ESA
- 2008

- Piotr Indyk, Andrew McGregor, +50 authors Matthias Westermann
- 2011

This document contains a list of open problems and research directions that have been suggested by participants at the Bertinoro Workshop on Sublinear Algorithms (May 2011) and IITK Workshop on Algorithms for Processing Massive Data Sets (December 2009). Many of the questions were discussed at the workshop or were posed during presentations. Further details… (More)

- Bastian Degener, Joachim Gehweiler, Christiane Lammersen
- Algorithmica
- 2008

We present a deterministic kinetic data structure for the facility location problem that maintains a subset of the moving points as facilities such that, at any point of time, the accumulated cost for the whole point set is at most a constant factor larger than the optimal cost. In our scenario, each point can change its status between client and facility… (More)

We present a deterministic kinetic data structure for the facility location problem that maintains a subset of the moving points as facilities such that, at any point of time, the accumulated cost for the whole point set is at most a constant factor larger than the current optimal cost. At any point of time, the cost that arise for a point depends on the… (More)

- Johannes Blömer, Christiane Lammersen, Melanie Schmidt, Christian Sohler
- Algorithm Engineering
- 2016

Clustering is a basic process in data analysis. It aims to partition a set of objects into groups called clusters such that, ideally, objects in the same group are similar and objects in different groups are dissimilar to each other. There are many scenarios where such a partition is useful. It may, for example, be used to structure the data to allow… (More)