Spatial data mining is the process of identifying or extracting efficient, novel, potentially useful and ultimately understandable patterns from the spatial data set, the spatial clustering analysis is one of the most...
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Spatial data mining is the process of identifying or extracting efficient, novel, potentially useful and ultimately understandable patterns from the spatial data set, the spatial clustering analysis is one of the most important research directions in spatial data mining. clustering criterion implied in massive data can be discovered by spatial clustering analysis method which can be used to explore deeper level knowledge combined with other data mining methods and to improve the efficiency and quality of data mining. We studied clustering algorithms of area geographical entities based on geometric shape similarity. And we presented a similarity criterion of line segments shape and a criterion of area geographical entities comprehensively utilizing distance and geometric shape similarity. clustering algorithms based on these criterions are more suitable for clustering analysis of area geographical entities.
Mobile devices like smartphones and tablets have become an integral part of our everyday life. These devices often store private information, which needs to be protected. To preserve this data we mainly use passwords,...
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Mobile devices like smartphones and tablets have become an integral part of our everyday life. These devices often store private information, which needs to be protected. To preserve this data we mainly use passwords, codes or SMS confirmation. They are easy to use, however there is always a risk of forgetting the password and also the risk of an impostor. On the other hand, there are other methods to identify a person, which overcome these threats. Biometric methods use the person itself to verify its identity. Many mobile devices like smartphones or tablets already have an implementation of biometric systems, but their usage often caused problems like shorter battery life, because of their computational complexity. Here a client-server architecture can be used, where the recognition process is divided into computational part running on the server and the acquisitional part running on the mobile device. In this paper a client-server face recognition system is presented with several clustering algorithms like k-means, self-organizing map etc. used for automatic training sample selection. The paper provides a comparative study of these algorithms and their impact on the implemented systems success rate.
Market segmentation is one of the most important area of knowledge-based marketing. In banks, it is really a challenging task as data bases are large and multidimensional. In the paper we consider cluster analysis, wh...
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Market segmentation is one of the most important area of knowledge-based marketing. In banks, it is really a challenging task as data bases are large and multidimensional. In the paper we consider cluster analysis, which is the methodology, the most often applied in this area. We compare clustering algorithms in cases of high dimensionality with noise. We discuss using three algorithms: density based DBSCAN, k-means and based on it two-phase clustering process. We compare algorithms concerning their effectiveness and scalability. Some experiments with exemplary bank data sets are presented.
clustering of nodes provides an efficient means of establishing a hierarchical structure in mobile ad hoc networks. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resultin...
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ISBN:
(纸本)9781424452446
clustering of nodes provides an efficient means of establishing a hierarchical structure in mobile ad hoc networks. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance; the clustering schemes for mobile ad hoc networks therefore aim at handling topology maintenance, managing node movement or reducing overhead. This paper presents the reasons for clustering algorithms in ad hoc networks, as well as a short survey of the basic ideas and priorities of existing clustering algorithms.
Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint co...
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Crowdsourcing-based localization has attracted wide research concern to the metropolitan-scale positioning. However, crowdsourcing-based fingerprints collection with assorted mobile smart devices brings fingerprint confusion, which significantly degrades the localization accuracy. To solve the device diversity problem, many solutions have been raised like the Device-clustering algorithm. Based on macro Device-Cluster (DC) rather than natural device, DC algorithm maintains less device types and slight calibration overhead. Despite high positioning accuracy, the selection of suitable clustering algorithms in DC system becomes another puzzle. In this paper, we reshape the novel Device-clustering algorithm to enhance the indoor positioning by comparing the application of different clustering algorithms. The experimental result indicates the reliability of DC strategy in broad clustering scheme as well as the suitable locating process corresponding to distinct environment.
The author introduces a near-real-time method of image processing in a PC-based environment. A segmentation technique based on unsupervised classification is implemented. A prototype for the detection of ice formation...
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The author introduces a near-real-time method of image processing in a PC-based environment. A segmentation technique based on unsupervised classification is implemented. A prototype for the detection of ice formation on the external tank (ET) of the Space Shuttle is being developed. The objective is to be able to do an online classification of the ET images into distinct regions denoting ice, frost, wet or dry areas. The images are acquired with an infrared camera and digitized before being processed by a computer to yield a false color-coded pattern, with each color representing a region. A two-monitor PC based setup is used for image processing. Various techniques for classification both supervised and unsupervised, are being investigated for developing a methodology. The implementation of two adaptive algorithms for image segmentation is discussed. The K-means algorithm is compared to another algorithm based on adaptive estimation of region boundaries.< >
Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. In this paper, an improved Fuz...
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ISBN:
(纸本)9781424482313
Fuzzy c-means algorithm (FCM) based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. In this paper, an improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance by taking a new threshold value and a new convergent process is proposed. The experimental results of three real data sets containing image classification show that our proposed new algorithm has the better performance.
The authors study sensor failure in noise-perturbed discrete-time linear systems represented by the usual state space model Kalman filtering. The Bayesian approach to failure detection is used. The best estimates are ...
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The authors study sensor failure in noise-perturbed discrete-time linear systems represented by the usual state space model Kalman filtering. The Bayesian approach to failure detection is used. The best estimates are obtained from the outputs of a linearly growing bank of Kalman filters (KFs), giving conditional distributions which are Gaussian mixtures. A method originally introduced by D.J. Salmond (1989, 1990) for dealing with clutter in target tracking problems is used here for combining components of this mixture in a way which causes minimum distortion. By using this, an approximate algorithm can be derived, which uses no more than a fixed number of KFs. The algorithm is straightforward to implement and demonstrated excellent performance.< >
In this work, we develop a new method of setting the input to reservoir and reservoir to reservoir weights in echo state machines. We use a clustering technique which we have previously developed as a pre-processing s...
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Hierarchical addresses are fundamental to the scalability of Internet routing. The explosive of the Internet has strained the initial two-level hierarchy and led to the development of more flexible divisions between l...
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ISBN:
(纸本)0818690143
Hierarchical addresses are fundamental to the scalability of Internet routing. The explosive of the Internet has strained the initial two-level hierarchy and led to the development of more flexible divisions between levels (CIDR) and larger addresses (IPv6). Equally, important are algorithms and protocols to systematically assign addresses with appropriate hierarchical structure to allow route aggregation. This paper describes and analyzes two algorithms for clustering network nodes into a multi-level address hierarchy. We evaluate the resulting address assignment with respect to routing table size, path length and concentration of traffic. We also explicitly recognize the need for "robustness" or "slack" the assignment to accommodate future changes in topology. Our evaluation includes both single- and multi-domain topologies.
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