%0 Journal Article %1 Beringer2006180 %A Beringer, Jürgen %A Hüllermeier, Eyke %D 2006 %J Data & Knowledge Engineering %K clustering online stream %N 2 %P 180 - 204 %R 10.1016/j.datak.2005.05.009 %T Online clustering of parallel data streams %U http://www.sciencedirect.com/science/article/pii/S0169023X05000819 %V 58 %X In recent years, the management and processing of so-called data streams has become a topic of active research in several fields of computer science such as, e.g., distributed systems, database systems, and data mining. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In this paper, we consider the problem of clustering parallel streams of real-valued data, that is to say, continuously evolving time series. In other words, we are interested in grouping data streams the evolution over time of which is similar in a specific sense. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating not more than a constant time delay. For this purpose, we develop an efficient online version of the classical K-means clustering algorithm. Our method’s efficiency is mainly due to a scalable online transformation of the original data which allows for a fast computation of approximate distances between streams.

@article{Beringer2006180, abstract = {In recent years, the management and processing of so-called data streams has become a topic of active research in several fields of computer science such as, e.g., distributed systems, database systems, and data mining. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In this paper, we consider the problem of clustering parallel streams of real-valued data, that is to say, continuously evolving time series. In other words, we are interested in grouping data streams the evolution over time of which is similar in a specific sense. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating not more than a constant time delay. For this purpose, we develop an efficient online version of the classical K-means clustering algorithm. Our method’s efficiency is mainly due to a scalable online transformation of the original data which allows for a fast computation of approximate distances between streams.}, added-at = {2012-06-19T17:46:24.000+0200}, author = {Beringer, Jürgen and Hüllermeier, Eyke}, biburl = {https://www.bibsonomy.org/bibtex/2371ee00b48901f64fac820e05059af7c/mboley}, description = {ScienceDirect.com - Data & Knowledge Engineering - Online clustering of parallel data streams}, doi = {10.1016/j.datak.2005.05.009}, interhash = {d5806dbbee8894812608b30fddb41c01}, intrahash = {371ee00b48901f64fac820e05059af7c}, issn = {0169-023X}, journal = {Data & Knowledge Engineering}, keywords = {clustering online stream}, number = 2, pages = {180 - 204}, timestamp = {2012-06-19T17:46:24.000+0200}, title = {Online clustering of parallel data streams}, url = {http://www.sciencedirect.com/science/article/pii/S0169023X05000819}, volume = 58, year = 2006 }