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2nd International Conference on Cloud Computing and Security (ICCCS)

作者： Yang, Yun Yang, Borui Wei, Wei Zhang, Baochang Beihang Univ Beijing 100191 Peoples R China Logist Engn Univ Chongqing Peoples R China

ISBN: (纸本)9783319486741;9783319486734

This paper focuses on human facial expression recognition in video sequences. Different from the methods of two-dimensional image recognition and three-dimensional spatial-temporal interest point detection, our approach highlights human facial expression recognition in complex spatial-temporal video datasets. The major challenge in facial expression recognition is how to obtain a feature dictionary from extracted cube pixel windows based on clustering algorithm. In this paper, our contributions are mainly concentrated on two aspects. Firstly, we combine discrete linear filter with key parameters selection procedure to extract 3D cuboids. Secondly, we propose a novel seed spot selection method to optimize K-means clustering algorithm. The proposed algorithms are evaluated on open databases. The results show that our approach can achieve outstanding results and the proposed approach is significantly effective.

关键词： Facial expression recognition Interest point detection clustering algorithm

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clustering algorithm of Hierarchical Structures in Large-Scale Wireless Sensor and Actuator Networks

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JOURNAL OF COMMUNICATIONS AND NETWORKS 2015年第5期17卷 473-481页

作者： Pham Tran Anh Quang Kim, Dong-Seong IRISA INRIA Rennes Res Ctr Rennes France Kumoh Natl Inst Technol Sch Elect Engn Seoul South Korea

In this study, we propose a clustering algorithm to enhance the performance of wireless sensor and actuator networks (WSANs). In each cluster, a multi-level hierarchical structure can be applied to reduce energy consumption. In addition to the cluster head, some nodes can be selected as intermediate nodes (INs). Each IN manages a subcluster that includes its neighbors. INs aggregate data from members in its subcluster, then send them to the cluster head. The selection of intermediate nodes aiming to optimize energy consumption can be considered high computational complexity mixed-integer linear programming. Therefore, a heuristic lowest energy path searching algorithm is proposed to reduce computational time. Moreover, a channel assignment scheme for sub-clusters is proposed to minimize interference between neighboring subclusters, thereby increasing aggregated throughput. Simulation results confirm that the proposed scheme can prolong network lifetime in WSANs.

关键词： clustering algorithm hierarchical structure large-scale mixed-integer linear programming wireless sensor network

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The analysis and research of clustering algorithm based on PCA

The analysis and research of clustering algorithm based on P...

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IEEE International Conference on Electronic Measurement & Instruments

作者： Liu Ni Si Jinhang Department of Intelligent Manufacturing Sichuan University Arts and Science Dazhou China Department of Mechanical and Electrical Engineering Xidian University Xi'an China

clustering analysis has been applied in many fields as a key technology in data analysis and processing. A lot of clustering algorithms which have their own advantages and disadvantages have been presented by many scholars. A clustering algorithm framework based on the principal component analysis is presented in this paper. Three steps are taken in the clustering algorithm such as following. Firstly, the data set are dealt with by the way of the principal component analysis (PCA), then the main components are selected to construct the new data set according to the analysis results, finally, the new data set is clustered by the way of DBSCAN. In the algorithm, the dimensions of the new data set are reduced. On account of the lower dimension data set, the amount of calculation is greatly reduced so as to improve the efficiency of the algorithm. The analyzing results of the algorithm simulation and conclusion are also given in this paper. The numerical simulations show that our algorithm is suitable for the occasion on which the clusters have the obvious difference between each other and this clustering algorithm can produce perfect clustering effect for the data set which is extracted by the principal component.

关键词： clustering algorithm PCA DBSCAN

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Applying network-free renormalization and clustering algorithms to reveal the crack evolution laws of laterally loaded composite T-joints

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ENGINEERING FRACTURE MECHANICS 2024年 307卷

作者： Shen, Zijie Xu, Jianxun Zou, Xionghui Gao, Weicheng Liu, Wei Zhou, Guangchun Wu, Zhenyu Harbin Inst Technol Sch Civil Engn Harbin 150090 Peoples R China Harbin Inst Technol Dept Astronaut Sci & Mech Harbin 150001 Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Struct Dynam Behav & Control Minist Educ Harbin 150090 Peoples R China Harbin Inst Technol Sch Civil Engn Key Lab Smart Prevent & Mitigat Civil Engn Disaste Minist Ind & Informat Technol Harbin 150090 Peoples R China Commercial Aircraft Corp China Ltd COMAC Shanghai Peoples R China

Delamination or crack are standard failure patterns of composite T-joints. Most existing studies focus on pull-off loaded composite T-joint cracking but little focus on adverse conditions such as lateral loading. The mainstream composite T-joint research predicts the macroscale failure based on the composite's microscale/mesoscale crack starting and evolution. However, the cracking process is within the microscale, mesoscale, and macroscale, making detecting its starting based on phenomena complicated. This work attempts to directly model the macroscale deformation distribution from a thermodynamic perspective to reveal the cracking/failure evolution of a laterally loaded composite T-joint. Firstly, we conducted eight laterally loaded composite T-joint tests with different molding processes. Then, network-free renormalization was applied to construct Matrices (Modes) and Hamiltonians (Characteristic parameters) that can characterize their cracking evolution process. Further, a clustering algorithm was applied to reveal the cracking starting points (phase transition loads) embedded in the macroscale deformation distribution. We can verify its rationality by comparing the composite T-joints localized and systematic phase transition loads based on Wilson's phase transition theory. In summary, this work applies network-free renormalization and clustering algorithms from a thermodynamic perspective to reveal the cracking starting point embedded in the macroscale deformation distribution of the laterally loaded composite T-joints.

关键词： Composite T -joints Lateral loading Network-free renormalization clustering algorithm Phase transition loads

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Autonomous clustering by fast find of mass and distance peaks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5336-5349页

作者： Yang, Jie Lin, Chin-Teng Australian AI Institute FEIT University of Technology Sydney Computational Intelligence and Brain Computer Interface Lab Australia

clustering is an essential analytical tool across a wide range of scientific fields, including biology, chemistry, astronomy, and pattern recognition. This paper introduces a novel clustering algorithm as a competitive alternative to existing methods, based on the intuitive principle that a cluster should merge with its nearest neighbor with a higher mass, unless both clusters have relatively large masses and the distance between them is also substantial. By identifying peaks in mass and distance, the algorithm effectively detects and removes incorrect mergers. The proposed method is entirely parameter-free, enabling it to autonomously recognize various cluster types, determine the optimal number of clusters, and identify noise. Extensive experiments on synthetic and real-world data sets demonstrate the algorithm's versatility and consistently strong performance compared to other state-of-the-art methods. © 1979-2012 IEEE.

关键词： clustering algorithms Partitioning algorithms Noise Nearest Neighbor Methods Merging Torque Biology Approximation algorithms Accuracy High Dimensional Data clustering Parameter Free Data Analysis Unsupervised Learning clustering algorithm Real World Data Large Mass Real World Datasets Hierarchical clustering Distance Matrix Image Dataset Density Estimation Cutoff Distance Agglomerative clustering Challenging Dataset Average Rank Hierarchical algorithm High Dimensional Datasets Abnormal Connectivity Hierarchical Tree Hierarchical clustering algorithm Recent algorithms Square Of The Distance Agglomerative Method Automatic Determination Cluster Mass Automatic clustering Global Mean Value Optical Character Recognition Hierarchical clustering Method Cell Tracking Partitional clustering Comprehensive Evaluation Image Recognition

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A Graph clustering algorithm Based on the Intra Vertex Adjacent Ratio

A Graph Clustering Algorithm Based on the Intra Vertex Adjac...

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2016 International Conference on Applied Mathematics, Simulation and Modelling(AMSM2016)

作者： Haili Guo Deqiang Wang Beijing City University Dalian Maritime University

ISBN: (纸本)9781510833890

This paper proposes a new clustering coefficient based on the number of connecting vertices within a cluster(called inter vertex adjacent ratio, IVAR) and proposes a new clustering algorithm based on IVAR. Finally, the analysis of algorithm applicability shows that the algorithm is more applicative and effective for regular graphs than the vertex-clustering algorithm based on intra connection ratio [L. Moussiades and A. Vakali, clustering dense graph: A web site graph paradigm. Information Processing and Management, vol. 46, pp.247-267, 2010].

关键词： component graph clustering algorithm intra vertex connection ratio

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Applied Biological Data Mining Based on Improved K-means clustering algorithm And KNN Classifier in Protein Sub-cellular Localization

Applied Biological Data Mining Based on Improved K-means Clu...

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2016 IEEE 7th International Conference on Software Engineering and Service Science

作者： Zhenfeng Lei Shunfang Wang School of Information Science and Engineering Yunnan University

With the completion of the human genome sequencing,a large number of data especially amino acid sequences floods into biological *** to analyze these data quickly and even predict the structure and function of protein correctly have become hot topics in recent *** this paper,we mainly study K-means clustering algorithm and KNN classifier in amino acid sequences of complicated data,which are applied in the prediction of protein sub-cellular *** many cases,fuzzy boundary and unbalance are frequently appeared among biological *** accuracy will be lower,if we make a prediction through traditional KNN and K-means clustering algorithm ***,in order to make clear the unbalance,we propose the within-class thought to make sure that training samples in each class around the testing sample are selected and we introduce membership to tell which class the testing sample belongs ***,we bring in rough sets and membership to solve the fuzzy ***,we apply correlation coefficient in the rough sets to better reflect the relationship among data *** experimental results based on protein sub-cellular localization prediction show that the methods proposed newly better work than the traditional methods.

关键词： data mining clustering algorithm classification algorithm protein sub-cellular localization

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Space delay-tolerant network routing algorithm based on node clustering and social attributes

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AD HOC NETWORKS 2024年 155卷

作者： Cong, Ligang Ding, Huiying Xie, Nannan Wei, Xianhao Changchun Univ Sci & Technol Sch Comp Sci & Technol Satellite Rd Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Jilin Prov Key Lab Network & Informat Secur Satellite Rd Changchun 130022 Peoples R China

Efficient space delay-tolerant network (DTN) routing algorithms have been a hot issue in space network-related research. In this paper, for the problems of high algorithm complexity and high network overhead in space DTN routing algorithms, we introduce a node clustering algorithm and satellite node social attributes in the space DTN routing process and design a spatial delay-tolerant network routing algorithm (CD-SDTN) based on node clustering and social attributes. The algorithm designs the social attributes of the satellite nodes based on the connection frequency of the satellite nodes with the ground station and divides the community using the Circle-k-means algorithm. In the routing process, the center satellite node with the highest activity in the community manages the messages received by the satellite nodes located in the community and plans the forwarding of messages within and between communities. Simulation results show that the CD-SDTN routing algorithm has some advantages in terms of delivery rate, average delay and network overhead compared with Epidemic and Prophet, especially when the number of nodes in the satellite network is large.

关键词： Space network Delay-tolerant network routing algorithm clustering algorithm Social attribute

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A clustering algorithm for sample data based on environmental pollution characteristics

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ATMOSPHERIC ENVIRONMENT 2015年第Apr.期107卷 194-203页

作者： Chen, Mei Wang, Pengfei Chen, Qiang Wu, Jiadong Chen, Xiaoyun Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China Lanzhou Univ Coll Atmospher Sci Lanzhou 730000 Peoples R China

Environmental pollution has become an issue of serious international concern in recent years. Among the receptor-oriented pollution models, CMB, PMF, UNMIX, and PCA are widely used as source apportionment models. To improve the accuracy of source apportionment and classify the sample data for these models, this study proposes an easy-to-use, high-dimensional EPC algorithm that not only organizes all of the sample data into different groups according to the similarities in pollution characteristics such as pollution sources and concentrations but also simultaneously detects outliers. The main clustering process consists of selecting the first unlabelled point as the cluster centre, then assigning each data point in the sample dataset to its most similar cluster centre according to both the user-defined threshold and the value of similarity function in each iteration, and finally modifying the clusters using a method similar to k-Means. The validity and accuracy of the algorithm are tested using both real and synthetic datasets, which makes the EPC algorithm practical and effective for appropriately classifying sample data for source apportionment models and helpful for better understanding and interpreting the sources of pollution. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Environmental pollution High-dimensional sample data Pollution characteristics clustering algorithm

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A Cloud-Friendly RFID Trajectory clustering algorithm in Uncertain Environments

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2015年第8期26卷 2075-2088页

作者： Wu, Yanbo Shen, Hong Sheng, Quan Z. Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

In the emerging environment of the Internet of Things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in mining useful information from RFID trajectories. RFID data usually contain a considerable degree of uncertainty caused by various factors such as hardware flaws, transmission faults and environment instability. In this paper, we propose an efficient clustering algorithm that is much less sensitive to noise and outliers than the existing methods. To better facilitate the emerging cloud computing resources, our algorithm is designed cloud-friendly so that it can be easily adopted in a cloud environment. The scalability and efficiency of the proposed algorithm are demonstrated through an extensive set of experimental studies.

关键词： Internet of Things radio frequency identification (RFID) uncertainty clustering algorithm cloud computing

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