Clustering Multiple Data Streams

In recent years, data streams analysis has gained a lot of attention due to the growth of applicative fields generating huge amount of temporal data. In this paper we will focus on the clustering of multiple streams. We propose a new strategy which aims at grouping similar streams and, together, at computing summaries of the incoming data. This is performed by means of a divide and conquer approach where a continuously updated graph collects information on incoming data and an off-line partitioning algorithm provides the final clustering structure. An application on real data sets corroborates the effectiveness of the proposal.

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