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Today´s applications and research issues in data mining have to deal with continuous, possibly infinite streams of data, arriving at high velocity. Web traffic, sensor measurements and stock trading data are just examples of these daily-increasing applications. This thesis presents and evaluates novel methods for an efficient subspace clustering of high-dimensional data streams. Primarily, efficient models of advanced clustering tasks are for the first time contributed for big data streams.

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