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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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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The Internet is drastically becoming part of our life as well as work. Every aspect of life is somehow associated with Internet due to which communication and technology needs to be getting advanced day by day. With t...
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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.
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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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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This paper presents two algorithms, one for clustering a set of interconnected nodes and the other for forming a linear placement of clustered interconnected nodes. The linear placement algorithm requires the output o...
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ISBN:
(纸本)0897912675
This paper presents two algorithms, one for clustering a set of interconnected nodes and the other for forming a linear placement of clustered interconnected nodes. The linear placement algorithm requires the output of clustering as an input. The two algorithms were designed to analyze the structure of digital logic for automatic placement of logic functions on a MOS/LSI chip as part of an automatic layoui, system8 and so far have only been used for that application. However, the clustering algorithm could be used to analyze any undirected graph. Both algorithms are noniterative and provide very good results with small amounts of computer time.
clustering is a primitive and important operator that analyzes a given dataset to discover its hidden patterns and features. Because datasets are usually updated dynamically (i.e., it accepts continuous insertions and...
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The continuous development of modern technology has gradually changed the way computers transmit information. Network information is in a world of mobile information transmission. While large data brings convenience t...
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This paper presents a new clustering algorithm inspired by Newtonian gravity that iteratively groups data and eliminates outliers. In particular, we impose a grid over the region of interest and define a particle with...
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