Correlation Clustering in Data Streams

Correlation Clustering in Data Streams

In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters whereas the end-points of positive-weight edges are typically in the same cluster. We present polyn...

Clustering Data Streams

W e study clustering under the data stream model of computation where: given a sequence of points, the objective is to maintain a consistently good clustering of the sequence observed so far, using a small amount of memory and time. The data stream model i s relevant to new classes of applications involving massive data sets, such as web click stream analysis and multimedia data analysis. W e g...

Clustering Parallel Data Streams

Clustering Geometric Data Streams

Using recent knowledge in data stream clustering we present a modified approach to the facility location problem in the context of geometric data streams. We give insight to the existing algorithm from a less mathematical point of view, focusing on understanding and practical use, namely by computer graphics experts. We propose a modification of the original data stream k-median clustering to s...

Clustering categorical data streams

The data stream model has been defined for new classes of applications involving massive data being generated at a fast pace. Web click stream analysis and detection of network intrusions are two examples. Cluster analysis on data streams becomes more difficult, because the data objects in a data stream must be accessed in order and can be read only once or few times with limited resources. Rec...