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SUHDSA: Secure, Useful, and High-Performance Data Stream Anonymization

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 9336-9347页

作者： Joo, Yongwan Kim, Soonseok Gangneung Wonju Natl Univ Ind Univ Cooperat Fdn Wonju 26403 South Korea Halla Univ Dept AI Informat Secur Wonju 26464 South Korea

This study addresses privacy concerns in real-time streaming data, including personal biometric signals and private information from sources such as real-time crime reporting, online sales transactions, and hospital patient-monitoring devices. Anonymization is crucial because it hides sensitive personal data. Achieving anonymity in real-time streaming data involves satisfying the unique demands of real-time scenarios, which is distinct from traditional methods. Specifically, security and minimal information loss must be maintained within a specified timeframe (referred to as the average delay time). The most recent solution in this context is the utility-based approach to data stream anonymization (UBDSA) algorithm developed by Sopaoglu and Abul. This study aims to enhance the performance of UBDSA by introducing a secure, useful, and high-performance data stream anonymization (SUHDSA) algorithm. SUHDSA outperforms UBDSA in terms of runtime and information loss while still ensuring privacy protection and an average delay time. The experimental results, using the same dataset and cluster size as in a previous UBDSA study, demonstrate significant performance improvements with the proposed algorithm. It achieves a minimum runtime of 24.05 s and a maximum runtime of 29.88 s, with information loss rates ranging from 14% to 77%. These results surpass the performance of the previous UBDSA algorithm.

关键词： Data privacy Real-time systems Information integrity Information filtering Delays clustering algorithms Security Data models Runtime Protection Anonymization privacy real-time stream data utility

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Characterization of Mobility Patterns With a Hierarchical clustering of Origin-Destination GPS Taxi Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 12700-12710页

作者： Heredia, Cristobal Moreno, Sebastian Yushimito, Wilfredo F. Univ Adolfo Ibanez Fac Sci & Engn Valparaiso 2520000 Chile

clustering taxi data is commonly used to understand spatial patterns of urban mobility. In this paper, we propose a new clustering model called Origin-Destination-means (OD-means). OD-means is a hierarchical adaptive k-means algorithm based on origin-destination pairs. In the first layer of the hierarchy, the clusters are separated automatically based on the variation of the within-cluster distance of each cluster until convergence. The second layer of the hierarchy corresponds to the sub clustering process of small clusters based on the distance between the origin and destination of each cluster. The algorithm is tested on a large data set of taxi GPS data from Santiago, Chile, and compared to other clustering algorithms. In contrast to them, our proposed model is capable of detecting general and local travel patterns in the city due to its hierarchical structure.

关键词： Public transportation Global Positioning System clustering methods clustering algorithms Data models Urban areas Data mining Machine learning taxi GPS data hierarchical clustering urban mobility patterns

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Self-Guided Deep Multiview Subspace clustering via Consensus Affinity Regularization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第12期52卷 12734-12744页

作者： Li, Kai Liu, Hongfu Zhang, Yulun Li, Kunpeng Fu, Yun Northeastern Univ Coll Engn Dept Elect & Comp Engn Boston MA 02115 USA Brandeis Univ Dept Comp Sci Waltham MA 02453 USA Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

Multiview subspace clustering (MVSC) leverages the complementary information among different views of multiview data and seeks a consensus subspace clustering result better than that using any individual view. Though proved effective in some cases, existing MVSC methods often obtain unsatisfactory results since they perform subspace analysis with raw features that are often of high dimensions and contain noises. To remedy this, we propose a self-guided deep multiview subspace clustering (SDMSC) model that performs joint deep feature embedding and subspace analysis. SDMSC comprehensively explores multiview data and strives to obtain a consensus data affinity relationship agreed by features from not only all views but also all intermediate embedding spaces. With more constraints being cast, the desirable data affinity relationship is supposed to be more reliably recovered. Besides, to secure effective deep feature embedding without label supervision, we propose to use the data affinity relationship obtained with raw features as the supervision signals to self-guide the embedding process. With this strategy, the risk that our deep clustering model being trapped in bad local minima is reduced, bringing us satisfactory clustering results in a higher possibility. The experiments on seven widely used datasets show the proposed method significantly outperforms the state-of-the-art clustering methods. Our code is available at https://***/kailigo/***.

关键词： Data models clustering methods Neural networks Analytical models clustering algorithms Reliability Task analysis Deep clustering multiview clustering (MVC) subspace clustering

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clustering with Stable Pattern Concepts 12

Clustering with Stable Pattern Concepts

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12th International Workshop "What can FCA do for Artificial Intelligence?, FCA4AI 2024

作者： Dudyrev, Egor Zueva, Mariia Kuznetsov, Sergei O. Napoli, Amedeo Université de Lorraine CNRS LORIA NancyF-54000 France HSE University 20 Myasnitskaya St Moscow101000 Russia

clustering aims at finding disjoint groups of similar objects in data and is one major task in Machine Learning. It is also gaining more attention in Formal Concept Analysis community in these last years. This paper proposes an original approach to the clustering of complex data based on Formal Concept Analysis (FCA) and Pattern Structures. Stable concepts are considered as cluster candidates and the SOFIA algorithm is used to discover the set of stable concepts in linear time. Then an algorithm inspired by a rare itemset mining algorithm is designed to build a clustering with good properties, i.e., high internal cohesion within a cluster and high external separation between the clusters. Some interestingness measures allowing us to choose the best clustering are discussed. Finally the present approach is compared to some other well-known algorithms such as KMeans, DBScan, and Optic. © 2024 Copyright for this paper by its authors.

关键词： clustering algorithms

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Cutting the Unnecessary Long Tail: Cost-Effective Big Data clustering in the Cloud

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IEEE TRANSACTIONS ON CLOUD COMPUTING 2022年第1期10卷 292-303页

作者： Li, Dongwei Wang, Shuliang Gao, Nan He, Qiang Yang, Yun Beijing Inst Technol Sch Comp Sci Beijing 100811 Haidian Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic 3122 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia

clustering big data often requires tremendous computational resources where cloud computing is undoubtedly one of the promising solutions. However, the computation cost in the cloud can be unexpectedly high if it cannot be managed properly. The long tail phenomenon has been observed widely in the big data clustering area, which indicates that the majority of time is often consumed in the middle to late stages in the clustering process. In this research, we try to cut the unnecessary long tail in the clustering process to achieve a sufficiently satisfactory accuracy at the lowest possible computation cost. A novel approach is proposed to achieve cost-effective big data clustering in the cloud. By training the regression model with the sampling data, we can make widely used k-means and EM (Expectation-Maximization) algorithms stop automatically at an early point when the desired accuracy is obtained. Experiments are conducted on four popular data sets and the results demonstrate that both k-means and EM algorithms can achieve high cost-effectiveness in the cloud with our proposed approach. For example, in the case studies with the much more efficient k-means algorithm, we find that achieving a 99 percent accuracy needs only 47.71-71.14 percent of the computation cost required for achieving a 100 percent accuracy while the less efficient EM algorithm needs 16.69-32.04 percent of the computation cost. To put that into perspective, in the United States land use classification example, our approach can save up to $94,687.49 for the government in each use.

关键词： Cloud computing cost-effectiveness clustering algorithms big data data mining

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Consensus Nature Inspired clustering of Single-Cell RNA-Sequencing Data

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IEEE ACCESS 2022年 10卷 98079-98094页

作者： Abou El-Naga, Amany H. Sayed, Sabah Salah, Akram Mohsen, Heba Future Univ Egypt Fac Comp & Informat Technol Dept Comp Sci Cairo 11835 Egypt Cairo Univ Fac Comp & Artificial Intelligence Dept Comp Sci Giza 12613 Egypt

Single-cell RNA sequencing (scRNA-seq) enables quantification of mRNA expression at the level of individual cells. scRNA-seq uncovers the disparity of cellular heterogeneity giving insights about the expression profiles of distinct cells revealing cellular differentiation. The rapid advancements in scRNA-seq technologies enable researchers to exploit questions regarding cancer heterogeneity and tumor microenvironment. The process of analyzing mainly clustering scRNA-seq data is computationally challenging due to its noisy high dimensionality nature. In this paper, a computational clustering approach is proposed to cluster scRNA-seq data based on consensus clustering using swarm intelligent optimization algorithms to accurately recognize cell subtypes. The proposed approach uses variational auto-encoders to handle the curse of dimensionality, as it operates to create a latent biologically relevant feature space representing the original data. The new latent space is then clustered using Particle Swarm Optimization Algorithm, Multi-Verse Optimization Algorithm and Grey Wolf Optimization Algorithm. A consensus solution is found using solutions returned by the swarm intelligent algorithms. The proposed approach automatically derives the number of clusters without any prior knowledge. To evaluate the performance of the proposed approach a total of four datasets have been used then a comparison against the existing methods in literature has been performed. Experimental results show that the proposed approach performs better than widely most used tools, achieving an adjusted rand index of.95,.75,.88,.9 for Biase, Goolam, Melanoma cancer and Lung cancer datasets respectively.

关键词： clustering algorithms Sequential analysis Search problems RNA Particle swarm optimization Cells (biology) Metaheuristics Single-cell RNA-seq automatic clustering unsupervised learning swarm intelligence metaheuristic algorithms consensus clustering

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Multi sound source joint localization algorithm for moving targets with multi-path coherent sources based on DOA clustering algorithm

Multi sound source joint localization algorithm for moving t...

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2024 International Conference on Signal Processing and Communication Security, ICSPCS 2024

作者： Shen, Wuqiang Long, Zhenyue Cui, Lei Zhang, Jinbo Guangdong Power Grid Company Limited Guangdong Guangzhou510000 China Joint Laboratory on Cyberspace Security China Southern Power Grid Guangdong Guangzhou510000 China

ISBN: (纸本)9781510681699

The conventional multi-path coherent source moving target location method focuses on signal processing. Although it reduces the location interference, part of the sound source information may become invalid, affecting the location accuracy. Therefore, a joint localization algorithm based on DOA clustering algorithm for multi-path coherent source moving target and multiple sound sources is designed. Map the DOA sound source feature interval of the moving target of multi-path coherent source, extract the DOA feature of the multi-path coherent source from the received signal, and provide basic conditions for subsequent sound source localization. Based on the DOA clustering algorithm, a joint localization model of multiple sound sources of source moving targets is constructed. With DOA characteristics as the basic condition, different sound source signals are separated through the clustering algorithm to ensure the accuracy of source moving target localization. Solve the optimal vector of multi sound source coherent source moving target joint positioning array, determine the optimal position of each sound source receiving point in the source moving array, and achieve multi sound source joint precise positioning. Simulation experiments show that the algorithm has higher positioning accuracy and can be applied in real life. © 2024 SPIE.

关键词： clustering algorithms

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Time Series clustering in High-Noise Conditions in the Data

Time Series Clustering in High-Noise Conditions in the Data

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2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Murtazin, Timur Ismagilov, Timur Ufa State Petroleum Technological University Ufa Russia

ISBN: (纸本)9798350395013

Time series clustering algorithm of a change in process parameters based on statistical homogeneity of time series fragment of a single cluster is suggested in the article. The selected clusters characterize stationary modes, which can be considered as basics of the situational modeling tasks, control and diagnostics of the process object state. For time series clustering in high-noise conditions in the measurement data of technological parameters the frequency-time disintegration on the basis of wavelet function and statistical characteristics analysis of approximating coefficients of discrete wavelet disintegration are used. The considered implementation of the suggested clustering algorithm is based on the time series of changes in the process parameter of an actual technological object with a high level of noise in relation to the useful signal. The algorithm allows to form a set of values of process parameters corresponding to the object's steady working mode, which are used in the task of identifying the technological situation. © 2024 IEEE.

关键词： clustering algorithms

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Path Tracking and Handling Stability Coordinated Control of 4WS and DYC for Distributed In-Wheel Motor Drive Electric Vehicle Under Extreme Conditions

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18402-18417页

作者： Zhang, Haichuan Wang, Shu Zhao, Xuan Zheng, Zichen Changan Univ Sch Automobile Xian 710000 Shannxi Peoples R China

How to improve the path tracking ability and handling stability under critical maneuvers is an important research issue for distributed in-wheel motor drive electric vehicle (IWMDEV). This article proposes a novel coordinated control architecture of active four-wheel steering (4WS) system and direct yaw moment control (DYC) system for IWMDEV. Firstly, a novel shared steering control framework for 4WS system is built, which includes two players, namely, the active front steering (AFS)and active rear steering (ARS). The non-cooperative game theory-based control strategy between AFS and ARS is designed to provide more lateral stability for the path following control. Secondly, a linear time-varying model predictive control (LTV-MPC) is used to determine the direct yaw moment of the vehicle. Then, on the basis of comprehensive consideration of system characteristics and control objective performance requirement, the coordination controller based on K-means Density Peak clustering and Particle Swarm Optimization (KDPC-PSO) algorithm is put forward to calculate optimal weight coefficient between the ARS system and DYC system. Finally, the Carsim/Simulink co-simulation and Hardware-in-the Loop (HIL) experiment results show that the proposed control system can effectively improve the path tracking ability and handling stability of IWMDEV under extreme conditions.

关键词： Stability criteria Wheels Control systems Vehicle dynamics clustering algorithms Tires Game theory In-wheel motor drive electric vehicle (IWMDEV) path tracking handling stability coordinated control

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Optimized Fuzzy Logic Based Energy-Efficient Geographical Data Routing in Internet of Things

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IEEE ACCESS 2024年 12卷 18913-18930页

作者： Aravind, Kalavagunta Maddikunta, Praveen Kumar Reddy Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

The Internet of Things (IoT) is being the key strategic enabler for realizing the vision of smart cities by allowing everyday objects to be connected through wireless sensor networks (WSNs). In large-scale WSNs, scalability, versatility, path performance, mobility support, and lower routing protocol overhead are all desirable characteristics. Given that GPS devices extract device locations approximately, geographic-oriented multicast routing techniques were selected because to their lower overhead. However, it is discovered that the current geographic-oriented routing models have several drawbacks. The sensor nodes' uneven energy consumption and high routing overhead have a significant impact on the lifespan and efficiency of the network. The aim of this work provides an energy-efficient geographic (EEG) routing protocol based on the given 6-fold-objective function. The best routes are chosen during EEG routing by fuzzy logic that has been optimized for membership functions. Here, the best route selection takes into account QoS, trust, energy, distance, delay, and overhead. In this work, Harris Hawk's Optimization (HHO) is utilized for optimization purposes. Furthermore, in the case of group 2, the mean performance of the proposed work is 25.6 %, 6.3%, 2.19%, 25.6%, 12.85%, and 10.64% better than that of CSO, EHO, MFO, WOA, DA, and SLnO, respectively. Lastly, the suggested work's performance is evaluated using several metrics in comparison to other traditional methods.

关键词： Routing Wireless sensor networks Routing protocols Optimization Internet of Things Energy efficiency clustering algorithms Fuzzy logic IoT WSN energy-efficient geographic (EEG) routing optimal path selection fuzzy logic system HHO model

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