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Recent advances in data collecting devices and data storage systems are continuously offering cheaper possibilities for gathering and storing increasingly bigger volumes of data. Similar improvements in the processing power and data bases enabled the accessibility to a large variety of complex data. Data mining is the task of extracting useful patterns and previously unknown knowledge out of this voluminous, various data. This thesis focuses on the data mining task of clustering, ie grouping objects into clusters such that similar objects are assigned to the same cluster while dissimilar ones are assigned to different clusters. While traditional clustering algorithms merely considered static data, 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 data, click streams, surveillance data, sensor measurements, customer profile data and stock trading are only some examples of these daily-increasing applications.