Online clustering of parallel data streams

Online clustering of parallel data streams

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 stre...

Clustering Parallel Data Streams

Online Clustering of Data Streams

We consider the problem of clustering data streams. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating but a constant time delay. For this purpose, we develop an efficient online version of the classical...

Fuzzy Clustering of Parallel Data Streams

The management and processing of so-called data streams has recently become a topic of active research in several fields of computer science, notably 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 ...

Probability Density Grid-based Online Clustering for Uncertain Data Streams

Most existing stream clustering algorithms adopt the online component and offline component. The disadvantage of two-phase algorithms is that they can not generate the final clusters online and the accurate clustering results need to be got through the offline analysis. Furthermore, the clustering algorithms for uncertain data streams are incompetent to find clusters of arbitrary shapes accordi...