作者：
Hänsch, RonnyJäger, MarcHellwich, Olaf
Department of Electrical Engineering Computer Science Computer Vision and Remote Sensing Sekr FR 3-1 Franklinstr. 28/29 BerlinD-10587 Germany
The basic assumption of clustering is that the observed data embodies a specific structure. This structure is represented by groups of data points, which are relativly isolated within the feature space. The goal of cl...
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In this paper, we investigate three types of c-means clustering algorithms with a conditionally positive definite (cpd) kernel. One is based on hard c-means and two are based on standard and entropy-regularized fuzzy ...
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In this paper, we investigate three types of c-means clustering algorithms with a conditionally positive definite (cpd) kernel. One is based on hard c-means and two are based on standard and entropy-regularized fuzzy c-means. First, based on a cpd kernel describing a squared Euclidean distance between data in feature space, these algorithms are derived from revised optimization problems of the conventional kernel c-means. Next, based on the relationship between the positive definite (pd) kernel and cpd kernel, the revised dissimilarity between a datum and a cluster center in the feature space is shown. Finally, it is shown that a cpd kernel c-means algorithm and a kernel c-means algorithm with a pd kernel derived from the cpd kernel are essentially identical to each other. Explicit mapping for a cpd kernel is also described geometrically.
With the powerful platform of cloud, it is attractive for an organization with massive data to outsource to a cloud provider. However, the data owner may have concerns about the privacy of its data. In this paper, we ...
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clustering algorithms play an important role in wireless self-organized network, especially the adaptive on-demand weighted clustering algorithm (AOW). But the AOW algorithm also has some limitations in most applicati...
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In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervise...
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We develop a new k-means clustering algorithm for data streams, which we call STREAMKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the k-MEANS++ algo...
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The most important property of the wireless sensor networks deployed for their numerous applications is their self-organizing property. The self-organizing property calls for network decomposition into clusters of spe...
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An effective tracking method is proposed to solve the problem that the electro-optical tracking system in Missile Range easily loses the real target during the target separation. Before target separation, the error co...
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Aiming at the problems of traditional clustering algorithms in the process of large amount of big data anomaly monitoring under the background of big data, a big data anomaly detection method based on improved fast de...
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Evolutionary clustering is an emerging research area addressing the problem of clustering dynamic data. An evolutionary clustering should take care of two conflicting criteria: preserving the current cluster quality a...
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