Discovering Clusters of Arbitrary Shapes and Densities in Data Streams

Discovering Clusters of Arbitrary Shapes and Densities in Data Streams

A density-based and grid-based approach to discover clusters in data streams

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The huge size of a continuously flowing data has put forward a number of challenges in data stream analysis. Exploration of the structure of streamed data represented a major challenge that resulted in introducing various clustering algorithms. However, current clustering algorithms still lack the ability to efficiently discover clusters of arbitrary densities in data streams. In this thesis, a new grid-based and density-based algorithm is proposed for clustering data streams. It addresses drawbacks of recent algorithms in discovering clusters of arbitrary densities. The algorithm uses an onli...