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When anomaly detection software is used as a data analysis tool, finding the hardest-to-detect anomalies is not the most critical task. Rather, it is often more important to make sure that those anomalies that are reported to the user are in fact interesting. If too many unremarkable data points are returned to the user labeled as candidate anomalies, the(More)
This paper deals with detecting change of distribution in multi-dimensional data sets. For a given baseline data set and a set of newly observed data points, we define a statistical test called the <i>density test</i> for deciding if the observed data points are sampled from the underlying distribution that produced the baseline data set. We define a test(More)
Given a spatial data set placed on an <i>n</i> x <i>n</i> grid, our goal is to find the rectangular regions within which subsets of the data set exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over each rectangular region in the grid, rank all of(More)
Given a spatial dataset placed on an <i>n</i> &#215;<i>n</i> grid, our goal is to find the rectangular regions within which subsets of the dataset exhibit anomalous behavior. We develop algorithms that, given any user-supplied arbitrary likelihood function, conduct a likelihood ratio hypothesis test (LRT) over each rectangular region in the grid, rank all(More)
Classic mixture models assume that the prevalence of the various mixture components is fixed and does not vary over time. This presents problems for applications where the goal is to learn how complex data distributions evolve. We develop models and Bayesian learning algorithms for inferring the temporal trends of the components in a mixture model as a(More)
  • Sandip S. Patil, Nitin Y. Suryavanshi, +13 authors Débora C. Muchaluat
  • 2011
Security with the intranet and internet becomes the very important issue as the total business is going to migrate towards the e-business. Security is not just about keeping people out of your network but also provides access into your network in the way you want to provide it, allowing the people to work together. When we are going to give the access to(More)
of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy FAULT TOLERANCE AND SCALABILITY OF DATA AGGREGATION IN SENSOR NETWORKS By Laukik Vilas Chitnis May 2008 Chair: Sanjay Ranka Cochair: Alin Dobra Major: Computer Engineering Sensor networks are finding(More)
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