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 say, continuously evolving time series. More specifically, 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 fuzzy C-means clustering algorithm. A fuzzy approach appears to be particularly useful for this type of application, in which the clustering structure is subject to continuous changes.