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A survey of models and applications of clustering algorithms in artificial intelligence scenarios

Academic Journal of Manufacturing Engineering 2018年第4期16卷 7-12页

作者： Jianfeng, Zou Huawei, Gao Fuzhou Changdu Information Tech LTD. China Fuzhou Changdu Information Tech LTD. China

The 21st century is a time of information, data, and knowledge. Information technology is changing every aspect of human society. From the perspective of machine learning, clustering, an important branch of data mining is an unsupervised learning method that attempts to find its distribution or pattern in unlabeled data sets. Generally, we think that the data points in the same cluster have more similarities than the data points of different clusters. Cluster analysis is a very important function in data mining. The so-called clustering analysis is to divide an abstract set or a physical set into several different categories according to some similar characteristics of the divided objects. This process is called the cluster analysis process. The core of cluster analysis is the clustering algorithm, which is a very important human behavior. This survey will integrate the latest research to propose the solutions of collecting the latest research. The overview will be the pipeline of the further research. © 2018 Editura Politechnica. All Rights Reserved.

关键词： clustering algorithms

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Distributed Incremental clustering algorithms: A Bibliometric and Word-Cloud Review Analysis

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Science and Technology Libraries 2020年第3期39卷 289-306页

作者： Mulay, Preeti Joshi, Rahul Chaudhari, Archana Symbiosis Institute of Technology Symbiosis International [Deemed University Pune India

"Incremental Learning (IL)" is the niche area of "Machine Learning." It is of utmost essential to keep learning incremental for ever-increasing data from all domains for effectual decisions, predications and solving problems. This can be achieved effectually by applying "Incremental clustering" methods on real-time data sources. IL can be achieved by "Incremental clustering" easily as well as effectively. To achieve worldwide data analysis related to the data and to achieve broader perspectives, it is essential to deploy "Incremental clustering" algorithms on distributed platforms, which will enable them to accept data from varied sources;analyze it and produce distributed worldwide solutions. This paper hence focuses on understanding the current status of "Distributed Incremental clustering algorithms (DICA)," its scope, limitations and other details so as to formulate better than the best algorithm in future. To enhance the analysis further Word-Clouds of impactful papers were explored and added in this paper, along with the details about platforms used to implement DICA by various upcoming researchers, readers and authors. © 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： clustering algorithms

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Between sound and spelling: Combining phonetics and clustering algorithms to improve target word recovery

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8686卷 438-499页

作者： Zampieri, Marcos de Amorim, Renato Cordeiro Saarland University Germany Birkbeck University of London and Glyndŵr University United Kingdom

In this paper we revisit the task of spell checking focusing on target word recovery. We propose a new approach that relies on phonetic information to improve the accuracy of clustering algorithms in identifying misspellings and generating accurate suggestions. The use of phonetic information is not new to the task of spell checking and it was used successfully in previous approaches. The combination of phonetics and cluster-based methods for spell checking was to our knowledge not yet explored and it is the new contribution of our work. We report an improvement of 8.16% accuracy when compared to a previously proposed spell checking approach. © Springer International Publishing Switzerland 2014.

关键词： clustering algorithms

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Evaluation of clustering algorithms for vector quantization in practical usage

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2014年第12期134卷 1936-1937页

作者： Sakakita, Hiromu Igarashi, Hiroaki Maeda, Junji Suzuki, Yukinori College of Information and Systems Muroran Institute of Technology 27-1 Mizumoto-cho Muroran050-8585 Japan

Image coding technologies are essential to use communication channels effectively. We have been studied vector quantization, because it does not cause deterioration in image quality in a high compression region and also has a small computational cost for image coding. The performance of vector quantization depends on a code book that is constructed using clustering algorithms. In this study, we evaluated popularly used typical clustering algorithms in terms of practical usage. Computational experiments showed that there is no notable difference in performance of the clustering algorithms in practical usage of vector quantization. © 2014 The Institute of Electrical Engineers of Japan.

关键词： clustering algorithms

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Semantic Web and Web Page clustering algorithms: A Landscape View

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第33期8卷 1-14页

作者： Obaid, Ahmed J. Chatterjee, Tanusree Bhattacharya, Abhishek Faculty of Computer Science and Mathematics University of Kufa Iraq Techno International NewTown India Institute of Engineering and Management India

The major evolution of the semantic web has become exchanging data between applications in all domains of activities. Based on this vision, different applications in recent days, e.g. in the fields of community web portals, social networking, e-learning, multimedia retrieval, etc. have been designed. Due to growing number of web services, clustering of web resources becomes a valuable tool for semantic web mining. clustering of internet objects like Internet web pages’ intimate new methods for grouping correlated content for better understanding and satisfies massive user query results in web pages’ search. Hence, web pages clustering algorithms should be able to handle massive irregular content and discover knowledge regardless of the web page complexity. These algorithms vary depending on the characteristics and data types. So, choosing the most appropriate algorithm is not an easy process as it should be accurate in terms of time and space complexity. Therefore, this paper rigorously surveys the most important algorithms of different types used for web page clustering. In addition, a comparative analysis of all such algorithms are provided in terms of several parameters. Finally, a brief discussion is provided on why web page clustering is important in emerging era of Semantic Web of Thing (SWoT) applications. Copyright © 2020 Ahmed J. Obaid et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

关键词： clustering algorithms

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Static and incremental overlapping clustering algorithms for large collections processing in GPU

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Informatica (Slovenia) 2018年第2期42卷 229-244页

作者： González-Soler, Lázaro Janier Pérez-Suárez, Airel Chang, Leonardo 7ma A 21406 Playa HavanaCP: 12200 Cuba

Pattern Recognition and Data Mining pose several problems in which, by their inherent nature, it is considered that an object can belong to more than one class;that is, clusters can overlap each other. OClustR and DClustR are overlapping clustering algorithms that have shown, in the task of documents clustering, the better tradeoff between quality of the clusters and efficiency, among the existing overlapping clustering algorithms. Despite the good achievements attained by both aforementioned algorithms, they are O(n2) so they could be less useful in applications dealing with a large number of documents. Moreover, although DClustR can efficiently process changes in an already clustered collection, the amount of memory it uses could make it not suitable for applications dealing with very large document collections. In this paper, two GPU-based parallel algorithms, named CUDA-OClus and CUDA-DClus, are proposed in order to enhance the efficiency of OClustR and DClustR, respectively, in problems dealing with a very large number of documents. The experimental evaluation conducted over several standard document collections showed the correctness of both CUDA-OClus and CUDA-DClus, and also their better performance in terms of efficiency and memory consumption. © 2018 Slovene Society Informatika. All rights reserved.

关键词： clustering algorithms

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Evaluation of clustering algorithms on GPU-Based Edge Computing Platforms

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SENSORS 2020年第21期20卷 6335.-6335.页

作者： Cecilia, Jose M. Cano, Juan-Carlos Morales-Garcia, Juan Llanes, Antonio Imbernon, Baldomero Univ Politecn Valencia UPV Comp Engn Dept DISCA Valencia 46022 Spain Univ Catolica Murcia UCAM Comp Sci Dept Murcia 30107 Spain

Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.

关键词： clustering algorithms IoT applications intelligent systems edge computing cloud computing GPU computing low-power

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A novel hybrid clustering algorithms with chaotic particle swarm optimization

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Journal of Computational Information Systems 2012年第21期8卷 8827-8834页

作者： Zhang, Jie Wang, Yuping Feng, Junhong School of Computer Science and Technology Xidian University Xi'an 710071 China Department of Computer Science and Technology Guangzhou University Sontan College Zengcheng 511370 China

In order to overcome the premature convergence in the particle swarm optimization algorithm, dynamically chaotic perturbation is introduced to form a dynamically chaotic PSO, briefly denoted as DCPSO. To get rid of the drawbacks of simply finding the convex cluster and being sensitive to the initial partitions in k-means algorithm, a novel hybrid clustering algorithm combined with the DCPSO is proposed. The difference between the work and the existing ones is that DCPSO is firstly introduced into k-means. From the experimental results made on several data sets, we can see that the proposed clustering algorithms can get completely rid of the shortcomings in k-means algorithms. © 2012 Binary Information Press.

关键词： clustering algorithms

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A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期110卷 104743-104743页

作者： Ezugwu, Absalom E. Ikotun, Abiodun M. Oyelade, Olaide O. Abualigah, Laith Agushaka, Jeffery O. Eke, Christopher I. Akinyelu, Andronicus A. Univ KwaZulu Natal Sch Comp Sci Pietermaritzburg Campus ZA-3201 Pietermaritzburg South Africa Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan Univ Sains Malaysia Sch Comp Sci George Town 11800 Pulau Pinang Malaysia Fed Univ Lafia Fac Nat Sci Dept Comp Sci Lafia Nigeria Univ Free State Dept Comp Sci & Informat ZA-9301 Bloemfontein South Africa

clustering is an essential tool in data mining research and applications. It is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. Several clustering techniques have been proposed and implemented, and most of them successfully find excellent quality or optimal clustering results in the domains mentioned earlier. However, there has been a gradual shift in the choice of clustering methods among domain experts and practitioners alike, which is precipitated by the fact that most traditional clustering algorithms still depend on the number of clusters provided a priori. These conventional clustering algorithms cannot effectively handle real-world data clustering analysis problems where the number of clusters in data objects cannot be easily identified. Also, they cannot effectively manage problems where the optimal number of clusters for a high-dimensional dataset cannot be easily determined. Therefore, there is a need for improved, flexible, and efficient clustering techniques. Recently, a variety of efficient clustering algorithms have been proposed in the literature, and these algorithms produced good results when evaluated on real-world clustering problems. This study presents an up-to-date systematic and comprehensive review of traditional and state-of-the-art clustering techniques for different domains. This survey considers clustering from a more practical perspective. It shows the outstanding role of clustering in various disciplines, such as education, marketing, medicine, biology, and bioinformatics. It also discusses the application of clustering to different fields attracting intensive efforts among the scientific community, such as big data, artificial intelligence, and robotics. This survey paper will be beneficial for both practitioners and researchers. It will serve as a good reference point for researchers and practitioners to desi

关键词： clustering clustering algorithms partitioning Data mining Hierarchical clustering Automatic clustering K-Means Optimization algorithms Machine learning Unsupervised learning Supervised learning clustering clustering algorithms partitioning Data mining Hierarchical clustering Automatic clustering K-Means Optimization algorithms Machine learning Unsupervised learning Supervised learning

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clustering-Based algorithms for Multivehicle Task Assignment in a Time-Invariant Drift Field

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IEEE ROBOTICS AND AUTOMATION LETTERS 2017年第4期2卷 2166-2173页

作者： Bai, Xiaoshan Yan, Weisheng Cao, Ming Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Shaanxi Peoples R China Univ Groningen Fac Sci & Engn NL-9747 AG Groningen Netherlands

This paper studies the multivehicle task assignment problem where several dispersed vehicles need to visit a set of target locations in a time-invariant drift field while trying to minimize the total travel time. Using optimal control theory, we first design a path planning algorithm to minimize the time for each vehicle to travel between two given locations in the drift field. The path planning algorithm provides the cost matrix for the target assignment, and generates routes once the target locations are assigned to a vehicle. Then, we propose several clustering strategies to assign the targets, and we use two metrics to determine the visiting sequence of the targets clustered to each vehicle. Mainly used to specify the minimum time for a vehicle to travel between any two target locations, the cost matrix is obtained using the path planning algorithm, and is in general asymmetric due to time-invariant currents of the drift field. We show that one of the clustering strategies can obtain a min-cost arborescence of the asymmetric target-vehicle graph where the weight of a directed edge between two vertices is the minimum travel time from one vertex to the other respecting the orientation. Using tools from graph theory, a lower bound on the optimal solution is found, which can be used to measure the proximity of a solution from the optimal. Furthermore, by integrating the target clustering strategies with the target visiting metrics, we obtain several task assignment algorithms. Among them, two algorithms guarantee that all the target locations will be visited within a computable maximal travel time, which is at most twice of the optimal when the cost matrix is symmetric. Finally, numerical simulations show that the algorithms can quickly lead to a solution that is close to the optimal.

关键词： Algorithm design and analysis clustering algorithms path planning task assignment vehicle routing

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