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检索条件"主题词=Clustering Algorithms"

共 51208 条记录，以下是471-480 订阅

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On Some Fuzzy clustering algorithms with Dimensionality Reduction

On Some Fuzzy Clustering Algorithms with Dimensionality Redu...

引用

International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Masanori Kawamura Yuchi Kanzawa Department of Information and Communications Engineering Shibaura Institute of Technology Toyosu Koto Tokyo

Certain fuzzy clustering algorithms involve dimensionality reduction techniques, such as principal component analysis (PCA), probabilistic principal component analysis (PPCA), and t-factor analysis (t-FA). Other fuzzification techniques have been applied to fuzzy clustering without dimensionality reduction. In this study, eleven fuzzy clustering algorithms are proposed based on five dimensionality reduction methods: PCA, PPCA, t-distribution-based PPCA, FA, and t-FA; and three fuzzification techniques: Bezdek-type, Kullback-Leibler divergence-regularization, and q-divergence-regularization. Based on numerical experiments using an artificial dataset, it is shown that some of the proposed methods outperforms the conventional methods on clustering accuracy.

关键词： Dimensionality reduction clustering algorithms Gaussian distribution Probabilistic logic Principal component analysis

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Applications of unsupervised clustering algorithms to aircraft identification using high range resolution radar

Applications of unsupervised clustering algorithms to aircra...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Dsung Tri Pham US Air Force Research Laboratory AFRL SNZ Dayton OH USA

ISBN: (纸本)0780344499

Identification of aircraft from high range resolution (HRR) radar range profiles requires a database of information capturing the variability of the individual range profiles as a function of viewing aspect. This database can be a collection of individual signatures or a collection of average signatures distributed over the region of viewing aspect of interest. An efficient database is one which captures the intrinsic variability of the HRR signatures without either excessive redundancy (over-characterization) typical of single-signature databases or without the loss of information (under-characterization) common when averaging arbitrary group of signatures. The identification of "natural" clustering of similar HRR signatures provides a means for creating efficient databases of either individual signatures or of signature templates. Using a k-means and the Kohonen self-organizing feature net, we identify the natural clustering of the HRR radar range profiles into groups of similar signatures based on the match quality metric (Euclidean distance) used within a Vector quantizer (VQ) classification algorithm. This greatly reduces the redundancy in such databases while retaining classification performance. Such clusters can be useful in template-based algorithms where groups of signatures are averaged to produce a template. Instead of basing the group of signatures to be averaged on arbitrary regions of viewing aspect, the averages are taken over the signatures contained in the natural clusters which have been Identified. The benefits of applying natural cluster identification to individual-signature HRR data preparation are decreased algorithm memory and computational requirements with a consequent decrease in the time required to perform identification calculations. When applied to template databases the benefits are improved identification performance. This paper describes the techniques used for identifying HRR signature clusters and describes the statistical proper

关键词： clustering algorithms Spatial databases Target recognition Military aircraft Aircraft manufacture Laboratories Airborne radar Distributed databases Euclidean distance Classification algorithms

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A comparison of modified k-means (MKM) and NN based real time adaptive clustering algorithms for articulatory space codebook formation

A comparison of modified k-means (MKM) and NN based real tim...

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International Conference on Spoken Language, ICSLP

作者： K.S. Ananthakrishnan Sch. of Electron. Eng. Univ. of South Australia Levels SA Australia

The paper proposes the use of a neural network based real time adaptive clustering algorithm for the formation of a codebook of limited set of acoustical representation of finite set of vocal tract shapes from an articulatory space. A modified k-means algorithm (MKM) used for clustering nearly 10000 vocal tract shapes into 1000 cluster centers to form a codebook of articulatory shapes is computationally intensive for the application. An investigative study on the use of the NN based algorithm over the MKM algorithm at the peripheral level, for an application on computer aided pronunciation education, suggests the former for less intensive computation, with the possibility of improving the performance of the system by implementing the algorithm using a dedicated neural computer. Preliminary results of this study are reported.

关键词： Neural networks clustering algorithms Shape Application software Computer peripherals Speech Partitioning algorithms Books Computer applications Neurofeedback

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Kernelized Cluster Validity Measures and Application to Evaluation of Different clustering algorithms

Kernelized Cluster Validity Measures and Application to Eval...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： R. Inokuchi T. Nakamura S. Miyamoto Risk Engineering University of Tsukuba Ibaraki Japan Department of Risk Engineering University of Tsukuba Ibaraki Japan

Many cluster validity measures have been proposed up to now, and it is realized that no universally best measure exists. In this paper we propose kernelized validity measures where a kernel means the kernel function used in support vector machines. Two measures are considered: one is the sum of the traces of the fuzzy covariances within clusters. Why we consider the trace instead of the determinant is that the calculation of the determinant will be ill-posed when kernelized, while the trace is sound and easily computed. The second is a kernelized Xie-Beni's measure. These two measures are applied to the determination of the number of clusters having nonlinear boundaries generated by kernelized clustering algorithms. Another application of the measures is the evaluation of robustness of different algorithms with respect to variations of initial values and changes of a parameter.

关键词： clustering algorithms Chromium Robustness Testing Kernel Support vector machines Fluctuations Stability Character generation Euclidean distance

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Takagi-Sugeno Fuzzy Modeling and Control of Nonlinear System with Adaptive clustering algorithms

Takagi-Sugeno Fuzzy Modeling and Control of Nonlinear System...

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International Conference on Modelling, Identification and Control (ICMIC)

作者： Kai Zhao Shurong Li Zhongjian Kang College of Information and Control Engineering Anyang Institute of Technology Anyang Qingdao China Automationschool of Beijing University of Posts and Telecommunications Beijing China College of Information and Control Engineering University of Petroleum Qingdao China

In order to control a nonlinear system, a model needs to be established to predict its behavior. At present, there are many methods for nonlinear system modeling. Among them T-S fuzzy prediction model has attracted extensive attention due to its better generalization and excellent approximation in the dense region. clustering algorithms can be used for the premise identification of the T-S model. But the optimal premise is not easy to be determined because of the difficulty to obtain optimal clustering number. For solving the shortcoming, a clustering validity function is described, based on which the clustering performance of adaptive fuzzy C-means clustering algorithm (adaptive FCM) is compared to that of the adaptive alternative fuzzy C-mean clustering algorithm (adaptive AFCM) with three datasets. Furthermore, two modeling algorithms for T-S fuzzy model using the adaptive FCM and the adaptive AFCM are designed, combining with the RLS, named adaptive FCM-RLS and adaptive AFCM-RLS. Finally, in order to demonstrate the effectiveness of the modeling methods in this paper, the T-S fuzzy model of a batch progress is constructed by adaptive FCM-RLS. With the T-S model, fuzzy generalized predictive controller is designed. Simulation results show that fuzzy-GPC controller has the better performances than GPC controller desisned with least square method.

关键词： clustering algorithms Adaptation models Predictive models Nonlinear systems Prediction algorithms Control systems Classification algorithms

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Fuzzy and possibilistic clustering algorithms based on generalized reformulation

Fuzzy and possibilistic clustering algorithms based on gener...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： N.B. Karayiannis Department of Electrical & Computer Engineering University of Houston Houston TX USA

This paper presents a new approach to fuzzy and possibilistic clustering based on reformulation. The reformulation of fuzzy c-means (FCM) algorithms provides the basis for reformulating entropy constrained fuzzy clustering (ECFC) algorithms. This paper also proposes a generalized reformulation function and interprets both FCM and ECFC algorithms as special cases of the broad family of fuzzy and possibilistic clustering algorithms resulting from this approach. New clustering algorithms are also developed and compared experimentally with FCM and ECFC algorithms.

关键词： clustering algorithms Entropy Prototypes Uncertainty Minimization methods Temperature sensors Probability Equations Fuzzy sets

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Application of clustering algorithms and self organising maps to classify electricity customers

Application of clustering algorithms and self organising map...

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Power Tech Conference

作者： G. Chicco R. Napoli F. Piglione Dipartimento di lngegneria Elettrica Industriale Palitecnico di Torino Torino Italy

In competitive electricity markets, the distribution service providers have been given new degrees of freedom in formulating dedicated tariff offers to be applied to properly defined customer classes. For this purpose, they may take advantages from identifying the consumption patterns of their customers and grouping together customers exhibiting similar load diagrams. In this paper, we report on results obtained by using various unsupervised clustering algorithms (modified follow-the-leader, k-means, fuzzy k-means and two types of hierarchical clustering) and the self organising map to group together customers having a similar electrical behaviour. The customer classification is investigated in the paper by considering a set of 234 non-residential customers. Each customer is characterised by a set of values extracted from the load diagrams in a given loading condition. The effectiveness of the classifications obtained with the various algorithms has been compared in terms of a set of adequacy indicators based on properly defined metrics, some of which have been originally developed by the authors. The results show that the modified follow-the-leader and one type of hierarchical clustering exhibit better characteristics than the other algorithms in terms of adequacy.

关键词： clustering algorithms Electricity supply industry Business Electronic mail Refining Data mining Condition monitoring Shape Neural networks Fuzzy logic

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Detection of malicious nodes in drone ad-hoc network based on supervised learning and clustering algorithms

Detection of malicious nodes in drone ad-hoc network based o...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Shanshan Sun Zuchao Ma Liang Liu Hang Gao Jianfei Peng College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Multi-drone swarm has been widely used in disaster monitoring, mapping and remote sensing, national defense military and other fields, and has become a research hotspot in recent years. Due to the openness of its operating environment, attackers can invade the control system to capture drone, and then carry out data attacks such as tamper attack, drop attack and replay attack in drone ad-hoc network, which causes a great threat to the security of drone network. Existing malicious nodes detection algorithms are not efficient when applied to drone ad-hoc network, for the following reasons: (1) The malicious node detection algorithms based on reputation usually adopt a static threshold to determine whether a node is malicious, which is inefficient in dynamic drone network. (2) Mutual cooperation based malicious node detection algorithms rely on the high meeting probability of nodes. In order to solve the above problems, we propose a Malicious Drones Detection Algorithm(MDA) based on supervised learning and clustering algorithms. The ground station calculates the reputation value of each routing path according to the received packets from different source nodes, and then evaluates the reputation value of drones with linear regression algorithm. Finally, gaussian clustering algorithm is used to cluster drones and find out malicious drones. Experiments were conducted in indoor and outdoor drone network. The experimental results indicate that the accuracy of MDA outperforms the existing methods by 10% 20%. And in the case of fewer malicious nodes, the accuracy can reach more than 90%, and the error rate is less than 10%.

关键词： Heuristic algorithms Linear regression Supervised learning clustering algorithms Ad hoc networks Detection algorithms Drones

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Comparative Analysis of clustering algorithms Based on Pixel Image Processing Using the Ssim Metric and Visual Analysis

Comparative Analysis of Clustering Algorithms Based on Pixel...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Kseniia Alexandrovna Potapova The Institute of Information Technologies MIREA – Russian Technological University Moscow Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The paper considers the problem of clustering pixels of a color raster image. The task is to compare the effectiveness of three different clustering methods: k-means, DBSCAN, agglomerative clustering. The k-means and agglomerative clustering algorithms consider different numbers of clusters: 2, 5, 10, 15, 20. The effectiveness is assessed using the SSIM metric and visual analysis of the resulting images.

关键词： Measurement Visualization Automation Image color analysis clustering algorithms Random access memory Process control Control systems Mathematical models Energy efficiency

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Energy-Aware clustering algorithms Used in Wireless Sensor Networks

Energy-Aware Clustering Algorithms Used in Wireless Sensor N...

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Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

作者： Muhammet Tay Arafat Senturk Duzce Universitesi Duzce TR

ISBN: (纸本)9781728110141

Wireless Sensor Networks (WSN) always need energy as a part of the areas they are used. Therefore, they must use their energy in the most efficient way. One of the most important roles in ensuring energy efficiency for the WSN is creating clusters between sensor nodes. Choosing the most appropriate sensor node as Cluster Head (CH) among the clustered sensors according to the predetermined criteria decreases the energy consumption. In this study, the most frequent clustering algorithms in the literature are included and these algorithms are compared based on certain metrics. As a result of the comparison, advantages and disadvantages of clustering algorithms are indicated.

关键词： Wireless sensor networks Energy efficiency Protocols clustering algorithms Barium Time division multiple access Voting

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