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A Deep Learning Approach for Identifying User Communities Based on Geographical Preferences and Its Applications to Urban and Environmental Planning

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ACM TRANSACTIONS ON SPATIAL algorithmS AND SYSTEMS 2020年第3期6卷 1–24页

作者： Ferreira, Danielle L. Nunes, Bruno A. A. Campos, Carlos Alberto, V Obraczka, Katia Univ Calif Santa Cruz UCSC 1156 High St Santa Cruz CA 95064 USA Univ Calif San Francisco UCSF 1500 Owens St 1630 Third St San Francisco CA USA Fed Univ State Rio de Janeiro UNIRIO Av Pasteur 296 Rio De Janeiro RJ Brazil Univ Calif San Francisco UCSF Dept Cardiol San Francisco CA 94143 USA

Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of more sophisticated models to characterize human mobility in urban areas. In this work, our main goal is to extract spatio-temporal features that characterize user mobility and, based on the similarity of these features, identify user communities. To this end, we propose a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images. These features are then fed to a clustering algorithm that identifies the underlying community structures. We use a diverse urban mobility dataset to validate the proposed framework. Our results show that the proposed framework is able to significantly increase the similarity between intra-community nodes (by up to 107%) as well as dissimilarity between inter-community nodes (up to 54%) when compared against no pre-processing of the datasets, i.e without pre-processing the datasets through any feature fusion method. Moreover, it was also able to reach up to 100% improvement when compared against community identification using Principal Component Analysis (PCA). Our results also show that the proposed approach yields significant increase in contact time amongst users belonging to the same community, by up to 80% when compared to the average contact time when not considering community structures, and by up to 150% when compared to the baseline. To the best of our knowledge, our proposal is the first to consider deep convolutional autoencoding to perform automatic extraction of non-linear spatio-temporal mobility features characterizing individual users from raw mobility datasets with the goal of identifying user communities.

关键词： Deep learning autoencoder convolutional neural networks T-sne principal component analysis clustering algorithm human mobility geographical preference user community mobility feature extraction

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Semisupervised Fuzzy clustering With Partition Information of Subsets

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2019年第9期27卷 1726-1737页

作者： Mei, Jian-Ping Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

Pairwise constraint is a type of side information that is widely considered in existing semisupervised clustering approaches. In this paper, we explore a new form of supervision for clustering. We consider the partition results of a number of subsets as additional information to assist clustering. Compared to the pairwise constraint, which only involves the "must-link" or "cannot-link" relationship of two objects, the partition of a subset of objects provides information about the group structure of more objects and hence can possibly serve as a more effective form of supervision for clustering. In this paper, we instantiate the idea of clustering with subset partitions under the fuzzy clustering framework for document categorization. The proposed fuzzy clustering approach is formulated to learn from the partition of subsets and has the ability to handle high-dimensional document data. Specifically, the partition results of subsets are collectively transformed into pairwise relationships, based on which a penalty term is constructed and incorporated into a cosine-distance-based fuzzy c-means approach. The experimental results on benchmark data sets demonstrate the effectiveness of the proposed approach for a semisupervised document clustering.

关键词： clustering algorithm document categorization fuzzy clustering semisupervised learning

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Data clustering: Theory, algorithms, and Applications, Second Edition

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丛书名： Mathematics in Industry

2020年

作者： Guojun Gan Chaoqun Ma Jianhong Wu

ISBN: (数字)9781611976335

ISBN: (纸本)9781611976328

Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments.

Data clustering: Theory, algorithms, and Applications, Second Edition

covers the basics of data clustering,
includes a list of popular clustering algorithms, and
provides program code that helps users implement clustering algorithms.

This book will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.

关键词： Data clustering Cluster analysis clustering algorithm Unsupervised learning Data mining Machine learning Pattern recognition Visualization Statistical data analysis Bioinformatics Knowledge discovery

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Sparse learning based on clustering by fast search and find of density peaks

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第23期78卷 33261-33277页

作者： Li, Pengqing Deng, Xuelian Zhang, Leyuan Gan, Jiangzhang Li, Jiaye Li, Yonggang Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Guangxi Peoples R China Guangxi Univ Chinese Med Coll Publ Hlth & Management Nanning Peoples R China

clustering by fast search and find of density peaks (CFSFDP) is a novel clustering algorithm proposed in recent years. The algorithm has the advantages of low computational complexity and high accuracy. However, the truncation distance d(c) needs to be determined according to user experience. Aiming to overcome these drawbacks, this paper proposes a new algorithm named Sparse learning based on clustering by fast search and find of density peaks (SL-CFSFDP). Compared to CFSFDP, the proposed algorithm can obtain d(c) automatically, and it uses sparse learning to determine the neighbors of each data point, removing irrelevant data points at the same time. SL-CFSFDP combines the local density and the distance delta(i) to automatically determine cluster centers, after which the remaining data points are assigned to clusters according to the local density and distance delta(i). Extensive experimental results on both synthetic and benchmark datasets show that SL-CFSFDP is superior to DBSCAN and CFSFDP.

关键词： Truncation distance Sparse learning Local density Density peaks clustering algorithm

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A Heterogeneous Nodes-Based Low Energy Adaptive clustering Hierarchy in Cognitive Radio Sensor Network

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IEEE ACCESS 2019年 7卷 132010-132026页

作者： Pei, Errong Pei, Jianliang Liu, Shan Cheng, Wei Li, Yonggang Zhang, Zhizhong Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Xinyu Univ Sch Mech & Elect Engn Xinyu 338004 Peoples R China

In order to cope with the resource shortage problem brought by cognitive radio technology in cognitive radio sensor network (CRSN), a new CRSN called heterogeneous CRSN (HCRSN) is proposed, where cognitive nodes (CNs) and sensor nodes (SNs) are separated and undertake different functions. Different from the existing clustering algorithms for homogeneous nodes based WSN, the clustering algorithm for HCRSN needs to consider the distribution of CNs among clusters such that enough high channel detection probability of each cluster can be guaranteed by the lowest deployment cost. Therefore, this paper first proposes a heterogeneous nodes based low energy adaptive clustering hierarchy (HLEACH) algorithm. In the algorithm, the sink node first updates the global information including the optimal number of clusters and average cluster radius and then broadcast it. Each CN calculates its competition radius after receiving the broadcasting information, and then start the competition for CHs based on the proposed competition rules. The elected CHs are finally censored targeting the optimal number of clusters to optimize the distribution of final CHs. In clusters' formation stage, non-CH CNs and SNs synthetically consider the distance and the connection degree of CHs such that the distribution of CNs among clusters and the energy consumption among CHs can be energy-efficiently balanced. The simulation results show that the proposed algorithm can not only effectively balance the distribution of CNs among clusters, guaranteeing enough high channel detection probability of each cluster and network energy utilization, but also balance the energy consumption among CHs, eventually prolong the network lifetime. Finally, the optimal deployment proportion of numbers and initial energy of the two types of nodes is also theoretical derived to maximize the energy utilization efificiency (i.e. the ratio of the network lifetime to the deployment cost).

关键词： WSN CRSN heterogeneous CRSN clustering algorithm cooperative spectrum sensing

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Ship Detection in High-Resolution SAR Images by clustering Spatially Enhanced Pixel Descriptor

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2019年第8期57卷 5407-5423页

作者： Lang, Haitao Xi, Yuyang Zhang, Xi Beijing Univ Chem Technol Sch Sci Dept Phys & Elect Beijing 100029 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Shandong Peoples R China

This paper proposes a new scheme for detecting ship targets in high-resolution (HR) single-channel synthetic aperture radar (SAR) images. By using the proposed spatially enhanced pixel descriptor (SEPD) and the modified density-based spatial clustering of application with noise (M-DBSCAN), this scheme can overcome typical challenges of ship detection in HR SAR images. Specifically, the proposed SEPD maps the representation for a given pixel in an SAR image into a high-dimensional feature space by embedding spatial and intensity information of its neighborhood synchronously. It enables the spatial structure information of ship targets and textural information of the sea surface to be preserved by the SEPD feature vector, leading to a significant improvement in the separability between ship targets and sea clutter. A statistical study shows that, in SEPD feature space, a large amount of pixels belonging to sea clutter gather densely in the low-value region and are surrounded by a tiny proportion of ship targets that are distributed sparsely in the high-value region. This distribution characteristic motivates us to apply a density-based clustering approach to distinguish ship targets from the sea clutter. To overcome the weakness of original DBSCAN clustering algorithm and make it suitable for the requirements of ship detection in SAR images, we propose the method of M-DBSCAN, which introduces three critical improvements, including a new dimensionality independent distance metric, a one-class clustering strategy, and an entirely deterministic approach to border points. A novel ship detector is proposed by applying M-DBSCAN to cluster pixels that are represented by SEPD descriptor. Comprehensive experiments demonstrate that the proposed method outperforms other intensity-based clustering methods (k-means and fuzzy c-means), and widely used intensity threshold-based method (constant false alarm rate detector) in most circumstances, and can effectively handle various chal

关键词： clustering algorithm constant false alarm rate (CFAR) modified density-based spatial clustering of applications with noise (M-DBSCAN) ship detection spatially enhanced pixel descriptor (SEPD) synthetic aperture radar (SAR)

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A Software-Defined clustering Mechanism for Underwater Acoustic Sensor Networks

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IEEE ACCESS 2019年 7卷 121742-121754页

作者： Wang, Jianping Gao, Guohong Qu, Peixin Chen, Wei Zhang, Shujing Zuo, Xiangang Yu, Zhou Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China

Underwater acoustic sensor networks (UASNs) are important technical means to explore the ocean realm. clustering is a crucial strategy which determines the performance and the lifetime of UASNs significantly. As a technological innovation, software defined networking (SDN) separates the data plane from the control plane to build a programmable network infrastructure. This paper presents a clustering mechanism based on SDN. Firstly, we introduce the architecture of software-defined UASN named SD-UASN. Secondly, we investigate hardware models of the underwater node and the surface controller. Thirdly, we define the communication procedure of SD-UASN, and implement the clustering mechanism. Finally, we perform simulations to verify the effectiveness of the proposed mechanism. The results reveal that a tradeoff of multiple constraints is achieved, and the performance of the clustering mechanism can be enhanced greatly. This work provides essential theoretical and technical support for software-defined UASNs.

关键词： Underwater acoustic sensor networks (UASNs) clustering algorithm software defined networking (SDN) software-defined UASN (SD-UASN) OpenFlow

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PlaceProfile: empregando análise visual e técnicas de agrupamento para criar perfis de regiões com base em pontos de interesse

PlaceProfile: empregando análise visual e técnicas de agru...

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作者： Christófano, Rafael Mariano Universidade Estadual Paulista

Entender como as atividades comerciais e sociais, bem como os pontos de interesse estão localizados em uma cidade, é essencial para planejar cidades eficientes em termos de mobilidade inteligente. Ao longo dos anos, o crescimento das fontes de dados de distintas redes sociais online têm permitido novas perspectivas para aplicativos que fornecem mecanismos para ajudar na compreensão de como as pessoas se deslocam entre as diferentes regiões de uma cidade. Para apoiar empresas e governos para melhor compreender e comparar regiões distintas de uma cidade, este trabalho propõe uma aplicação web chamado PlaceProfile para criação de perfis visuais de áreas de uma cidade com base em uma visualização iconográfica e para rotular áreas baseadas em algoritmos de agrupamento. Os resultados da visualização são sobrepostos no Google Maps para enriquecer o layout do mapa e a análise ajuda a entender o perfil da região em um relance. Além disso, o PlaceProfile coordena um gráfico de radar com áreas selecionadas pelo usuário para permitir a inspeção detalhada da frequência das categorias de pontos de interesse (POIs). Esta abordagem de visualizações coordenadas também apoia a explicabilidade de algoritmos de agrupamento por fornecer inspeções dos atributos usados para calcular semelhanças, ou seja, o número de pontos de interesse em cada categoria. Assim, uma variedade de pesquisas e aplicações voltadas a resolver problemas de mobilidade urbana podem se beneficiar dos resultados produzidos pelo *** how commercial and social activities as well as points of interest are located in a city is essential to plan efficient cities in terms of smart mobility. Over the years, the growth of data sources from distinct online social networks have enabled new perspectives to applications that provide mechanisms to aid in comprehension of how people displace between different regions within a city. To support enterprises and governments to better understand and compare disti

关键词： Perfil de áreas Cidades inteligentes Mobilidade inteligente Algoritmos de agrupamento POIs Pontos de Interesse Visualização Profilling areas Smart cities Smart mobility clustering algorithm Visualization Points of Interesting Dissertação de m

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Swarm intelligence-based secure high-order optimal density selection for industrial internet-of-things (IIoT) data on cloud environment

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第17期34卷 e4976-e4976页

作者： Primya, T. Subashini, G. Dr NGP Inst Technol Dept CSE Coimbatore Tamil Nadu India PSG Coll Technol Dept Robot & Automat Engn Coimbatore Tamil Nadu India

Industrial internet-of-things (IIoT) cloud computing gives users with the ease of outsourcing calculations and carrying the risk of privacy disclosure. To solve this issue, secure high-order clustering algorithm by fast search (SHOCFS) technique is introduced recently. The major factors of SHOCFS algorithm are speed improvement and detecting optimal density peaks. The optimal selection of density points in the SHOCFS algorithm still becomes an important issue to be considered. The swallow swarm optimization (SSO) is introduced in the proposed work for choosing optimal density peaks in clustering algorithm. This paper motivates to design a new secure cloud service system for cloud in IIoT environment. The secure high-order optimal density selection (SHODS3O-CFS) approach is proposed in hybrid cloud environment. The major contribution of the work is to develop an optimization-based secure clustering algorithm for finding the best density points in clustering algorithm. It may reduce the overlapping peak points in the cluster and increase the security of system in hybrid cloud. The experimental results of the proposed algorithm and existing methods are measured via clustering metrics such as accuracy, clustering center quality, and speed ratio. The metrics like clustering center quality, Rand index (RI), encryption time (ms), and speedup ratio have been used to measure the results of the proposed system and existing methods. From the results, it is concluded that the proposed work has achieved higher average RI of 93.4%, which is 29.68% and 17.4% higher when compared with the privacy preservation high-order clustering algorithm by fast search (PPHOCFS) and SHOCFS methods.

关键词： cloud computing security clustering algorithm cyber-physical systems (CPS) homomorphic encryption industrial internet of things (IIoT) privacy preserving swallow swarm optimization (SSO)

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An unsupervised and robust validity index for clustering analysis

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SOFT COMPUTING 2019年第20期23卷 10303-10319页

作者： Wang, Yaru Yue, Shihong Hao, Zhenhua Ding, Mingliang Li, Jia Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The evaluation of clustering results plays an important role in clustering analysis and usually is completed by a validity index or several. But currently existing validity indexes are supervised since they greatly depend on prior information, such as specified clustering algorithms and optimal initializations. Once the prior information is unavailable, the evaluating results of these supervised validity indexes are no longer guaranteed, which lead to that their applicable ranges are greatly limited. In this paper, we firstly propose an estimation of the lower and upper bounds of the number of within-cluster distances in any dataset, and then an unsupervised validity index without needing any clustering algorithm and initialization is presented. A group of typical simulated and real datasets with various characteristics validate the proposed index in an unsupervised way. Experimental results demonstrate that the proposed index has higher accuracy in most tested datasets and has advantages in robustness and runtime compared with the other existing validity indexes.

关键词： clustering analysis Validity index Within-cluster distances clustering algorithm

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