Schmidt, FlorianEhrenfeld, YannickAnna Puig and Renata Raidou2018-06-022018-06-022018978-3-03868-065-9https://doi.org/10.2312/eurp.20181122https://diglib.eg.org:443/handle/10.2312/eurp20181122Digitalization increases the opportunity to collect vast amounts of data in a large scale manner. In order to handle the information overload, data mining techniques like online clustering are performed. A lot of online clusterers are based on the concept of micro-clusters in order to represent the given data stream. Based on its definition, micro-clusters can be represented as an n-sphere. Online clustering algorithms like BIRCH or DenStream use different strategies for maintaining the micro-clusters in evolving time series, but using the same underlying key concept storing a summarized version of the data stream in their models. We propose ViMEC, an application for multidimensional micro-cluster visualization, giving the user the opportunity to gain understanding of the internal behaviour of the clustering model. For a given time frame, ViMEC gives the user three different types of visualizations presenting different levels of details: Overview, Pair-view and Detail-view. These views combine not only a summary and detail representations for the different dimensions, but also aim to show different relations between dimensions. Preliminary results show, that large data sets with up to 20,000 data points can be visualized within less than 20 seconds.Humancentered computingInformation visualizationVisualization toolkits10.2312/eurp.2018112229-31