Fig. 2From: S-RASTER: contraction clustering for evolving data streamsThe precision parameter \(\xi\) greatly influences clustering results of RASTER (best viewed in color). This illustration is based on retaining all data points (cf. Fig. 1d). With a precision of \(\xi = 0.90\) (top), all but the rightmost data set are clustered satisfactorily. Reducing the precision to \(\xi = 0.73\) (bottom) improves the results of that data set. It is a matter of debate which value of \(\xi\) led to a better result with the data set in the middle as a good case could be made for either result, depending on whether the goal of the user is to identify dense or sparse clusters. The data sets were taken from a collection of standard data sets for the evaluation of general-purpose clustering algorithms that are part of the machine learning library scikit-learn [27]. A more extensive discussion of these results is provided in a previous paper on RASTER [33]Back to article page