Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption

Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However

and also within the NSF-sponsored vBNS. This paper presents observations on the patterns and characteristics of wide-area Internet traffic, as recorded by MCI’s OC-3 traffic monitors. We report on measurements from two OC-3 trunks in MCI’s commercial Internet backbone over two time ranges (24-hour and 7

sharing many of the same goals as previous dis-tributed file systems, our design has been driven by obser-vations of our application workloads and technological envi-ronment, both current and anticipated, that reflect a marked departure from some earlier file system assumptions. This has led us

The large-scale behavior of routing in the Internet has gone virtually without any formal study, the exception being Chinoy's analysis of the dynamics of Internet routing information [Ch93]. We report on an analysis of 40,000 end-to-end route measurements conducted using repeated “traceroutes” between 37 Internet sites. We analyze the routing behavior for pathological conditions, routing stability, and routing symmetry. For pathologies, we characterize the prevalence of routing loops, erroneous routing, infrastructure failures, and temporary outages. We find that the likelihood of encountering a major routing pathology more than doubled between the end of 1994 and the end of 1995, rising from 1.5 % to 3.4%. For routing stability, we define two separate types of stability, “prevalence, ” meaning the overall likelihood that a particular route is encountered, and “persistence, ” the likelihood that a route remains unchanged over a long period of time. We find that Internet paths are heavily dominated by a single prevalent route, but that the time periods over which routes persist show wide variation, ranging from seconds up to days. About 2/3's of the Internet paths had routes persisting for either days or weeks. For routing symmetry, we look at the likelihood that a path through the Internet visits at least one different city in the two directions. At the end of 1995, this was the case half the time, and at least one different autonomous system was visited 30 % of the time.

Abstract-Mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, is generalized and analyzed in this paper. This generalization makes some k-means like clustering algorithms its special cases. It is shown that mean shift is a mode-seeking process on a surface constructed with a “shadow ” kernel. For Gaussian kernels, mean shift is a gradient mapping. Convergence is studied for mean shift iterations. Cluster analysis is treated as a deterministic problem of finding a fixed point of mean shift that characterizes the data. Applications in clustering and Hough transform are demon-strated. Mean shift is also considered as an evolutionary strategy that performs multistart global optimization. Index Terms-Mean shift, gradient descent, global optimiza-tion, Hough transform, cluster analysis, k-means clustering. I.

We present an unsupervised technique for visual learning which is based on density estimation in high-dimensional spaces using an eigenspace decomposition. Two types of density estimates are derived for modeling the training data: a multivariate Gaussian (for unimodal distributions) and a Mixture-of-Gaussians model (for multimodal distributions). These probability densities are then used to formulate a maximum-likelihood estimation framework for visual search and target detection for automatic object recognition and coding. Our learning technique is applied to the probabilistic visual modeling, detection, recognition, and coding of human faces and non-rigid objects such as hands.

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