Aparicio-Carrasco, Ana M.

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Comparación de algoritmos para clustering de "streams" de series de tiempo
(2012-05) Aparicio-Carrasco, Ana M.; Acuña-Fernández, Edgar; College of Arts and Sciences - Sciences; Urintsev, Alexander; González, Ana Carmen; Department of Mathematics; Valentín Rullán, Ricky
In recent years, technological advances have resulted in a huge increment in data production as in the evolution of methods that facilitated its collection. The data that arrive continuously and massively with infinite tendency are known as data streams. The source of these data is, for instance, sensors, bank personal transactions and automated measuring tools among others. The algorithms for processing this kind of data must provide rapid and real time responses, which implies that they must maintain a decision model all the time. The clustering of data streams by variables finds groups of variables (data streams) with similar behavior over time. In this work we compare two different approaches of algorithms for clustering of data streams by variables: ODAC, a divisive hierarchical algorithm and CORREL that operates over the Sliding Windows model and performs clustering by partitioning. Based on the experimental it is study concluded that ODAC outperforms CORREL because of its performance and independence from the distribution of data streams. However, it required a big amount of data points (“examples”) to discover the inherent clustering structure.