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检索条件"主题词=Affinity propagation clustering"

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Hierarchical Solving Method for Large Scale TSP Problems

Hierarchical Solving Method for Large Scale TSP Problems

引用

11th International Symposium on Neural Networks (ISNN)

作者： Jiang, Jingqing Gao, Jingying Li, Gaoyang Wu, Chunguo Pei, Zhili Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China Inner Mongolia Univ Nationalities Coll Math Tongliao Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130023 Peoples R China

ISBN: (纸本)9783319124360;9783319124353

This paper presents a hierarchical algorithm for solving large-scale traveling salesman problem (TSP), the algorithm first uses clustering algorithms to large-scale TSP problem into a number of small-scale collections of cities, and then put this TSP problem as a generalized traveling salesman problem (GTSP), convert solving large-scale TSP problem into solving GTSP and several small-scale TSP problems. Then all the sub-problems will be solved by ant colony algorithm and At last all the solutions of each sub-problem will be merged into the solution of the large-scale TSP problem by solution of GTSP. Experimental part we uses the traditional ant colony algorithm and new algorithm for solving large-scale TSP problem, numerical simulation results show that the proposed algorithm for large-scale TSP problem has a good effect, compared with the traditional ant colony algorithm, the solving efficiency has been significantly improved.

关键词： Traveling salesman problem Ant colony algorithm affinity propagation clustering K-means algorithm Generalized traveling salesman problem

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Optimization of Training Samples with affinity propagation Algorithm for Multi-class SVM Classification

Optimization of Training Samples with Affinity Propagation A...

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7th International Symposium on Neural Networks

作者： Lv, Guangjun Yin, Qian Xu, Bingxin Guo, Ping Beijing Normal Univ Image Proc & Pattern Recognit Lab Beijing 100875 Peoples R China

ISBN: (纸本)9783642133176

This paper presents a novel optimization method of training samples with affinity propagation (AP) clustering algorithm for multi-class Support Vector Machine (SVM) classification problem. The method of optimizing training samples is based on region clustering with affinity propagation algorithm. Then the multi-class support vector machines are trained for natural image classification with AP optimized samples. The feature space constructed in this paper is a composition of combined histogram with color, texture and edge descriptor of images. Experimental results show that better classification accuracy can be obtained by using the proposed method.

关键词： affinity propagation clustering multi-class Support Vector Machine natural image classification training sample optimization

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A Spatiotemporal 3D Fuzzy Modeling Method for A Rapid Thermal Processing System 40

A Spatiotemporal 3D Fuzzy Modeling Method for A Rapid Therma...

引用

40th Chinese Control Conference (CCC)

作者： Yan, Runbin Zhang, Xianxia Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200444 Peoples R China

ISBN: (纸本)9789881563804

Rapid thermal processing (RTP) is an emerging technology in the fabrication of semiconductor devices. RTP processes are usually nonlinear, time-varying dynamical, and spatiotemporal. A model is particularly important to an RTP for the purpose of control, optimization and prediction. In this paper, a two-level three-domain (3D) fuzzy modeling methodology is put forward for modeling an RTP system. The lower level is a 3D fuzzy model representing the RTP system, which naturally realizes the function of time-space separation and time-space synthesis in the form of 3D fuzzy rules. At the upper level, machine learning dominates the design of the 3D fuzzy model: learning of antecedent part using affinity propagation clustering and learning of consequent part using least-square algorithm. The proposed method is applied to model a typical RTP system RTCVD (rapid thermal chemical vapor deposition system). Compared with other modeling methods, simulation results show a better modeling performance.

关键词： Distributed parameter system affinity propagation clustering Fuzzy modeling Rapid thermal processing

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AUTOMATED IMAGE ANALYSIS AND INFERENCE OF GENE FUNCTION FROM HIGH - CONTENT SCREENS

AUTOMATED IMAGE ANALYSIS AND INFERENCE OF GENE FUNCTION FROM...

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21st European Signal Processing Conference (EUSIPCO)

作者： Raja, Priyanka J. Jacob, Justin Yoon, Byung-Jun Bartholomeusz, Geofferey Rao, Arvind Univ Texas MD Anderson Canc Ctr Dept Bioinformat & Computat Biol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Bioinformat & Computat Biol Houston TX 77030 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX USA

ISBN: (纸本)9780992862602

The study of tumor biology and heterogeneity is of high importance for identifying viable cancer therapeutics. A high content RNAi screen is carried out to identify genes that induce varied tumor morphologies. We present a novel automated pipeline to identify and interpret gene function by extracting morphological features of tumor cell aggregates, in large scale 3D RNAi screens. We use a "bag of words" based clustering approach to distinguish multiple phenotypes. Functional analysis of genes underlying the phenotypic clusters reveals the role of growth and invasion modulators in shaping tumor cell morphology and heterogeneity.

关键词： image analysis image processing machine learning high content screening textural features affinity propagation clustering earth movers distance

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A Fourier Space Basis Function Based 3D Modeling Method for Distributed Parameter Systems 40

A Fourier Space Basis Function Based 3D Modeling Method for ...

引用

40th Chinese Control Conference (CCC)

作者： Zhang, Xianxia Li, Zhiyuan Yan, Runbin Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200444 Peoples R China

ISBN: (纸本)9789881563804

A fourier space basis function based 3D fuzzy modeling method for distributed parameter systems is proposed in this paper. Unlike the traditional modeling method with model reduction, 3D fuzzy modeling integrates the time-space seperation and the time-space sysnsis into a 3D fuzzy model framework naturally. In this study, the construction of the 3D fuzzy model is of data-driven style. The antecedent parts of the 3D fuzzy rules is learned by affinity propagation clustering algorithm. Fourier space basis function is considered as the concequent set of the 3D fuzzy rule, and its parameters are learned by the gradient descent algorithm. The proposed modeling method is successful applied to a simulated parabolic rod reactor system. In comparison with other modeling methods, the simulation results validate the superiority of the proposed modeling method.

关键词： Fuzzy modeling 3D fuzzy system affinity propagation clustering Distributed parameter system

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Study on locations for electricity fee payment in rural power business regions driven by data 9

Study on locations for electricity fee payment in rural powe...

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9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Shi, Yan Li, Wenjie Fan, Xin Xie, Hongwei Ma, Hongbo East Inner Mongolia Elect Power Co Ltd Power Res Inst Hohhot 010020 Inner Mongolia Peoples R China Beijing KeDong Elect Power Control Syst Co Ltd Beijing 100094 Peoples R China

ISBN: (纸本)9781538630228

The study of location of the electricity payment point in rural electric power business area is the key issue to solve the "three rural issues", implement "One Belt and One Road", build a well-off society, while the big data environment provides a new research idea for the reasonable distribution of electricity payment point. Taking residents' electricity payment record and low voltage data detail of Ningcheng County in Chifeng for example, to achieve "electricity payment point for village-to-village", Firstly, based on affinity propagation clustering of the data points' "information transfer", we solve the candidate payment points for two kinds of voltage area, respectively. Then, use the set covering model to make quadratic optimization for candidate payment points, and obtain the final payment points. On this basis, we analyze the resources allocation of the power supply business hall, and provide decision support to optimizate location of the payment point in rural electric power business area for power supply company.

关键词： Data-driven affinity propagation clustering Set c overing model Electricity payment point for rural areas

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A Reduction SVM Classification Algorithm Based on Adaptive AP clustering Granulation

A Reduction SVM Classification Algorithm Based on Adaptive A...

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10th International Conference on Intelligent Computing (ICIC)

作者： Wei, Xiuxi Guangxi Int Business Vocat Coll Dept Informat Engn Nanning 530007 Peoples R China

ISBN: (纸本)9783319093390;9783319093383

The classification speed of SVM is inversely proportional to the number of Support Vectors (SVs). Therefore, the less SVs means the more sparseness and the higher classification speed. In order to reduce the number of SVs but without losing of generalization performance, a new algorithm called Classification Algorithm of Support Vector Machine based on Adaptive affinity propagation clustering Granulation (CSVM-AAPG) is proposed, which employs affinity propagation (AP) clustering algorithm to cluster the original SVs and the cluster centers are used as the new SVs, then aiming to minimize the classification gap between SVM and CSVM-AAPG, a quadratic programming model is built for obtaining the optimal coefficients of the new SVs. Meanwhile, it is proven that when clustering the original SVs, the minimal upper bound of the error between the original decision function and the fast decision function can be achieved by AP. Finally, experiments show that compared with original SVs, the number of SVs decreases and the speed of classification increases using CSVM-AAPG, while the loss of accuracy is in the acceptable level.

关键词： SVM Adaptive affinity propagation clustering Granulation Classification

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Feature Representation Based on Improved Word-Vector clustering Using AP and E²LSH 16th

Feature Representation Based on Improved Word-Vector Cluster...

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Joint Conference of the 16th Asia Simulation Conference / SCS International Autumn Simulation Multi-Conference (AsiaSim/SCS AutumnSim)

作者： Li, Hongmei Hao, Wenning Zhang, Hongjun Chen, Gang PLA Univ Sci & Technol Inst Command Informat Syst Nanjing Jiangsu Peoples R China

ISBN: (纸本)9789811026720;9789811026713

Deep learning model has witnessed its obvious advantage in feature representation and document retrival. However, the model only considered most frequent words as the input to learn latent features, which inevitably ignores lots of useful information contained in documents especially for high-dimensional documents. We introduce a novel method based on word-vector clustering to obtain low-dimensional semantic vectors of documents, as the input of deep learning model to improve the feature representation in the output layer. Firstly, word-vector, a kind of compact and distributed representation of words, is obtained by training neural network language model using word2vec. Then, we present a modified word-vector clustering method based on locality-sensitive hashing and affinity propagation, with a stronger adaptability and scalability for large scale and high dimensionality. Afterwards, each document is represented by the set of cluster centers as the input of deep learning model. Experimental results proved the proposed method improves the ability of feature representation of deep learning model and performs better on document retrieval task compared with traditional methods.

关键词： Deep learning model Distributed representation Word-vector clustering affinity propagation clustering Exact-Euclidean locality-sensitive hashing

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Design and Implement of Clicks Stream Analyzing Engine Based on clustering

Design and Implement of Clicks Stream Analyzing Engine Based...

引用

2011 International Conference on Computers, Communications, Control and Automation

作者： LI Yang ZHANG Liang College of Economics and Management Tianjin University of Science and Technology

ISBN: (纸本)9781612841021

Web usage mining involves application of data mining techniques to discover usage patterns from the web data. clustering of click streams in Web-logs can find Web visitor's using patterns, and categorize these visitors. Click streams is a typical data stream. In the field of data stream analysis, conventional methods seem not quite useful. Because neither they can adapt to the dynamic environment of data stream, nor the mining models and result s can meet users' needs. A grid and density based clustering method is proposed to effectively address the problem. This paper describes an analytical model to get the most preferable browsing paths of a web site through analysis of its clicks and design of customerbehavior analyzing engine based on clicks stream is proposed. Such model can predict behaviors of the web's customers to help make the commercial decisions..

关键词： affinity propagation clustering clicks analysis client behavior mining frequent pattern

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clustering Algorithm of Web Click Stream Frequency Pattern

Clustering Algorithm of Web Click Stream Frequency Pattern

引用

2011 International Conference on Computers, Communications, Control and Automation

作者： LI Yang ZHANG Liang College of Economics and Management Tianjin University of Science and Technology

ISBN: (纸本)9781612841021

Web usage mining involves application of data mining techniques to discover usage patterns from the web data. clustering is one of the important functions in web usage mining. clustering of click streams in Web-logs can find Web visitor's using patterns, and categorize these visitors. This document describes an analytical model to get the most preferable browsing paths of a web site through analysis of its clicks. Such model can predict behaviors of the web's customers to help make the commercial decisions. The model applies data mining in the analysis of web clicks stream. The objective of this paper is to provide support for commercial websites to understand the dynamic demand of the market situation.

关键词： affinity propagation clustering clicks analysis client behavior mining frequent pattern

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