David E. Patterson

Learn More
We describe an interactive method to generate a set of fuzzy clusters for classes of interest of a given, labeled data set. The presented method is therefore best suited for applications where the focus of analysis lies on a model for the minority class or for small to medium-sized data sets. The clustering algorithm creates one–dimensional models of the(More)
It is alten desirable to summarize a set of time series through typical shapes in order to analyze fhem. The algorithm presented here compares pieces of different time series in order to find similar shapes, The use of a fuzzy clustering technique based on fuzzy c-means aUows us to consider such subsets as beleng-ing to typical shapes with a degree of(More)
The main task of drug discovery is to find novel bioactive molecules, i.e., chemical compounds that, for example, protect human cells against a virus. One way to support solving this task is to analyze a database of known and tested molecules in order to find structural properties of molecules that determine whether a molecule will be active or inactive, so(More)
We describe an interactive way to generate a set of clusters for a given data set. The clustering is done by constructing local histograms, which can then be used to visualize , select, and fine-tune potential cluster candidates. The accompanying algorithm can also generate clusters automatically , allowing for an automatic or semi-automatic clustering(More)
  • 1