Matthew Bolaños

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In recent years, data streams have become an increasingly important area of research for the computer science, database and data mining communities. Data streams are ordered and potentially unbounded sequences of data points created by a typically non-stationary generation process. Common data mining tasks associated with data streams include clustering,(More)
Unsupervised identification of groups in large data sets is important for many machine learning and knowledge discovery applications. Conventional clustering approaches (k-means, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal(More)
Description A framework for data stream modeling and associated data mining tasks such as clustering and classification. Index 60 animation 3 animation Animates the plotting of a DSD and the clustering process Description Generates an animation of a data stream or a data steam clustering. Arguments dsd a DSD object dsc a DSC object macro a DSC_macro object(More)
In recent years, data streams have become an important area of research. Common data mining tasks involve classifying and clustering changing and evolving data over an extended period. Due to the complexity of changing data, however, it is dicult to produce real or simulated datasets that clearly exhibit behaviors such as merging and splitting of clusters.(More)
Description A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912. Index 60 animation 3 animation Animates the plotting of a DSD and the clustering process Description Generates an animation of a data stream or(More)
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