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VizOPTICS: Getting insights into OPTICS via interactive visual analysis

COMPUTERS & ELECTRICAL ENGINEERING 2023年第1期107卷

作者： Wu, Caixia Chen, Yi Dong, Yu Zhou, Fangfang Zhao, Ying Liang, Christy Jie Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China Univ Technol Sydney Sch Comp Sci Sydney NSW 2008 Australia Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

Ordering points to identify the clustering structure (OPTICS) is a density-based clustering algorithm that allows the exploration of the cluster structure in the dataset by outputting an ordered queue called cluster ordering. However, this nonexplicit output makes it greatly more difficult for practitioners to identify cluster patterns and obtain high-quality clusters. In this paper, we firstly investigate OPTICS in depth and identify the challenges facing users of OPTICS for cluster analysis through a pilot user study. Then, integrating human intelligence deeply with the machine intelligence of OPTICS, a visual analytics approach, VizOPTICS, is proposed to support practitioners in understanding and applying OPTICS to extract meaningful clustering results. It includes an ordered lattice plot for observing the generation process of cluster ordering, a density scatter plot for analyzing the cluster structure in datasets, and a dynamic reachability plot for optimizing clustering results, and also provides several interaction modes, such as selecting and highlighting, to help users analyze the cluster formation and algorithm operation processes interactively. Finally, we assess our approach through four case studies and a user evaluation study. The results demonstrate the effectiveness and efficiency of the system.

关键词： clustering algorithm OPTICS Cluster analysis Visualization Interaction Explainable machine learning

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Dynamical clustering in Electronic Commerce Systems via Optimization and Leadership Expansion

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第8期16卷 5327-5334页

作者： Li, Hui-Jia Bu, Zhan Wang, Zhen Cao, Jie Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Nanjing Univ Finance & Econ Jiangsu Prov Key Lab E Business Nanjing 210003 Peoples R China Northwestern Polytech Univ Ctr Opt IMagery Anal & Learning Xian 710072 Peoples R China

In many electronic commerce systems, detecting significant clusters is of great value to the analysis, design, and optimization of the commerce behaviors. In this article, we propose a new dynamical approach to detect the cluster configuration fast and accurately which can be applied to electronic commerce systems. First, we analyze the two-stage game in which the leader group members make contributions prior to the follower group, and propose an exact index, i.e., the leadership, to characterize the key leaders. Then an efficient dynamical system is used to guarantee the cluster configuration converges to an optimal state, which assigns each node to the corresponding cluster based on quality optimization, repeatedly. Our method is of high efficiency-the exponential term in the proposed dynamical system makes the convergence to be very fast with a nearly linear time. Extensive experiments on multiple types of datesets demonstrate the state-of-the-art performance of proposed method.

关键词： Computational complexity clustering algorithm dynamical systems electronic commerce systems game theory

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Quantum walks-based classification model with resistance for cloud computing attacks

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期232卷

作者： Wu, Xiaodong Jin, Zhigang Zhou, Junyi Duan, Chenxu Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Tianjin Univ Int Engn Inst Tianjin 300072 Peoples R China State Grid Tianjin Elect Power Co State Grid Tianjin Elect Power Co Informat & Commu Tianjin 300140 Peoples R China Tianjin Univ Sch Elect & Informat Engn Dept Commun Engn Tianjin 300072 Peoples R China

Cloud computing is considerably investigable and adoptable in both industry and academia, and Software Defined Networking (SDN) has been applied in cloud computing. Although SDN mitigates some security issues in cloud computing, new security issues related to its own architecture are also introduced. In this paper, we propose a quantum walks-based classification model which is available for intrusion detection in cloud computing. The proposed model concentrates feature information of data via Principal Component Analysis, and then aggregates the concentrated data in the way of quantum walks by a training-free clustering algorithm. The clustering algorithm constructs coin transformation and conditional shift transformation based on transition probabilities to move similar data toward each other. To enhance the usability of the proposed model in cloud computing security, we propose a new cloud architecture which adds security layer in SDN to ponder the protection of cloud computing fundamentally, and simplify transition probabilities equations of clustering algorithm without affecting clustering accuracy, decreasing the time complexity from O(nk2) to O(nk). The experimental results on popular datasets (Accuracy: 99.4% on InSDN, 95.8% on NSL-KDD, 98% on UNSW-NB15 and 96.4% on CSE-CIC-IDS2018) revealed that the proposed model is effective dealing with attacks on SDN-based cloud computing, and is able to maintain stable and excellent attack identification ability under different traffic intensities.

关键词： Classification model Quantum walks Cloud computing security Software defined networking clustering algorithm Intrusion detection

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Combination weighted clustering algorithms in cognitive radio networks

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2020年第23期32卷

作者： Li, Xiaoyan Lv, Zhigang Wang, Peng Sun, Mengyu Qiao, Mengyu Xian Technol Univ Sch Elect Informat Engn Xian 710021 Shaanxi Peoples R China

The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resources. A combination weighted clustering algorithm is proposed in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. First, a clustering algorithm considering the available channel, geographic location, and experienced data (used for collecting the behavior of secondary users (SUs) in the network and the evaluation on it) of SUs is put forward through analyzing the characteristics of the idle channels in cognitive network. Three factors, namely, average channel capacity, stability, and channel quality, are converted into quantifiable values. The cluster head is determined on the basis of the three factors. Then, the cluster members and gateway nodes are determined using the weighting formula and the location information of the cluster head. Results show that the proposed clustering algorithm can generate 15% more clusters than other algorithms and reduce 40% of network communication overhead when the transmission distance between cognitive users and the channel number change. Thus, the stability of the cluster structure is maintained and the communication overhead is decreased. This study provides references for the construction of the stable distributed cognitive network.

关键词： clustering algorithm cognitive radio combination weighting

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The application of artificial intelligence technology of 6G internet of things communication combined with drama language art

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年第4期12卷 864-870页

作者： Lyu, Bo He, Mengna Shenyang Normal Univ Inst Dramat Arts Shenyang 110034 Peoples R China

The purpose is to further promote the integration of advanced technology in the field of drama. Under the background of artificial intelligence of Internet of things, drama language is analyzed by using a clustering algorithm of data mining technology. The original K-means algorithm is compared with the improved K-means algorithm through the use and analysis of the corresponding data mining methods. Latent Dirichlet Allocation (LDA) topic model clustering is used to test the randomly selected texts. The results suggest that the improved algorithm is more reliable and efficient in data analysis and clustering effect, which proves the availability of the improved K-means algorithm. The specific research content can provide a scientific and effective reference for the follow-up research of text mining analysis.

关键词： Internet of things and artificial intelligence Data mining clustering algorithm K-means algorithm

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Application of Optimized Partitioning Around Medoid algorithm in Image Retrieval

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2021年第1期12卷 77-94页

作者： Jin, Yanxia Zhang, Xin Jia, Yao North Univ China Taiyuan Peoples R China Beihang Univ Beijing Peoples R China

In image retrieval, the major challenge is that the number of images in the gallery is large and irregular, which results in low retrieval accuracy. This paper analyzes the disadvantages of the PAM (partitioning around medoid) clustering algorithm in image data classification and the excessive consumption of time in the computation process of searching clustering representative objects using the PAM clustering algorithm. Fireworks particle swarm algorithm is utilized in the optimization process. PF-PAM algorithm, which is an improved PAM algorithm, is proposed and applied in image retrieval. First, extract the feature vector of the image in the gallery for the first clustering. Next, according to the clustering results, the most optimal cluster center is searched through the firework particle swarm algorithm to obtain the final clustering result. Finally, according to the incoming query image, determine the related image category and get similar images. The experimental comparison with other approaches shows that this method can effectively improve retrieval accuracy.

关键词： clustering algorithm Image Retrieval Particle Swarm Optimization

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Construction of intelligent supervision platform for college students' physical health for intelligent medical service decision-making

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FRONTIERS IN PHYSICS 2023年 11卷

作者： Guo, Fei Harbin Univ Sch Phys Educ Harbin Heilongjiang Peoples R China

Due to the rapid changes in current technology, machine learning and high-performance computing in medical applications also usher in new development opportunities. They are widely used in medical data analysis, diagnostic decision-making, disease prediction, disease assisted diagnosis, disease prognosis evaluation, new drug research and development, health management, and other fields. The impact of medical application on daily life is also increasing, which makes the use of intelligent medical service decision-making more extensive. However, with the continuous improvement and development of the population's physical fitness, the physical fitness of university students is deteriorating. Physical decline has become a common concern. Therefore, it is of great significance to investigate the physical condition of college students and find a more suitable method to promote the physical health of college students. It helps college students better engage in learning and life, enabling them to adapt to work faster and better meet the current social development needs for college students' physical fitness. For this reason, this paper proposes the idea of building a smart supervision platform for college students' physical health through smart medical service decision-making. Through empirical research on this platform, it is found that the method of building the platform proposed in this paper is more conducive to the improvement of college students' physical health. The excellent grade of freshmen in this platform is 5.4% higher than that of the traditional platform, and the excellent grade of sophomores in the test is 6.31% higher than that of the traditional platform, the excellent grade of college students' physical health test on this platform accounts for a higher proportion. The platform provides corresponding personalized sports programs through real-time monitoring of students' physical health, so as to realize teaching students in accordance with their aptitude,

关键词： college students physical health intelligent medical service decision-making clustering algorithm sports culture smart security service system

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Community energy storage system: Deep learning based optimal energy management solution for residential community

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JOURNAL OF ENERGY STORAGE 2023年 64卷

作者： Alam, Md. Morshed Bin Mofidul, Raihan Jang, Yeong Min Kookmin Univ Dept Elect Engn Seoul South Korea

The concept of community energy storage system (CESS) is required for the efficient and reliable utilization of renewable energy and flexible energy sharing among consumers. This paper proposes a novel approach to assess the practical benefits of CESS deployment in a residential community by decreasing the daily electricity cost and maximizing the self-consumption of PV energy. To this end, a deep-learning-based forecasting model, namely a bi-directional long short-term memory model, is implemented to predict the operational constraints and dependency. Furthermore, a hybrid optimization technique that comprises a clustering and optimization algorithm is developed in which the clustering algorithm ensures appropriate combinations of user groups to develop optimal control policies. Finally, the forecasting model is integrated with the hybrid optimization algorithm to find the optimal solution involving PV-CESS energy utilization. Numerical analyses are performed using real historic data of the energy demand and PV generation for three consecutive days considering different scenarios. The results demonstrate that the electricity costs and self-consumption associated with the CESS are lower and greater than those of an individual ESS system, respectively, with the daily electricity cost decreasing by 21.89%, 13.81%, and 7.66% in the three analyzed scenarios.

关键词： Energy management system Community energy storage system Optimal scheduling Optimization algorithm Bi-directional long short-term memory clustering algorithm

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MULTI-SOURCE AND HETEROGENEOUS ONLINE MUSIC EDUCATION MECHANISM: AN ARTIFICIAL INTELLIGENCE-DRIVEN APPROACH

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2023年第6期31卷 2340154-2340154页

作者： Yang, Yuanyuan Dolly, Raveena Judie Alassafi, Madini O. Slowik, Adam Alsaadi, Fawaz E. Xinxiang Univ Xinxiang 453003 Peoples R China Karunya Inst Technol & Sci Coimbatore 641114 Tamil Nadu India King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Technol Jeddah 21589 Saudi Arabia Koszalin Univ Technol Koszalin Poland

In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.

关键词： Artificial Intelligence Heterogeneous Online Music Education Personalized Recommendation Multimodal Network Model clustering algorithm

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An Ensemble of Geostatistical Simulated Realizations Using a clustering algorithm to Reproduce Model-Based Statistics: Case Study of a Gravel Reserve at Kivalina, Alaska

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MARINE GEORESOURCES & GEOTECHNOLOGY 2013年第3期31卷 225-241页

作者： Chatterjee, Snehamoy Bandopadhyay, Sukumar Natl Inst Technol Dept Min Engn Rourkela India Univ Alaska Fairbanks Coll Engn & Mines Fairbanks AK USA

This paper demonstrates the convergence of model-based statistics from multiple simulated realizations. Theoretically, the convergence of realization statistics is guaranteed over the number of realizations that are independent among themselves. The rate at which realization-based statistics converges with model-based statistics is important and must be assessed. However, due to poor selection of the random number generator, the generated realization might be far from mutual independence. We use the k-means clustering algorithm to select nearly independent realizations from a set of realization models. We apply the proposed algorithm to a coastal erosion problem in Alaska to estimate the amount of gravel.

关键词： clustering algorithm geostatistics gravel reserve Kivalina Alaska

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