David E. Patterson

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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)
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-size data sets. The clustering algorithm creates one-dimensional models of the(More)
Often, it is desirable to represent a set of time series through typical shapes in order to detect common patterns. The algorithm presented here compares pieces of a different time series in order to find such similar shapes. The use of a fuzzy clustering technique based on fuzzy c-means allows us to detect shapes that belong to a certain group of typical(More)
6. 7 INTRODUCTION Classical learning algorithms create models of data in an uncontrolled, non-interactive manner. Typically the user specifies some (method-dependent) parameters like di stance function or number of clusters that he/she likes to identify, followed by the app lication of the algorithm usi ng these settings. The process of the model generation(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)
In this paper we that allows to find Abstract present a variant of fuzzy c-means similar shapes in time series data in a scale-invariant fashion. We use data from protein mass spectrography to show how this approach finds areas of interest without a need for ad-hoc nomaliza-tions. When analyzing time series data, especially from biological domains such as(More)
New serotonin reuptake inhibitors are available for the treatment of affective disorders and sleep dysfunction in traumatic brain injury (TBI) patients. Commonly reported serotonergic side-effects include nausea, headache, dizziness, nervousness and orthostatic hypotension. Trazodone, a non-selective serotonin reuptake inhibitor, is often used in(More)
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