Adaptive gravitational clustering algorithm integrated with noise detection
Clustering analysis is frequently used in data mining, image processing, artificial intelligence, and so on. Traditional approaches heavily rely on manually configured parameters, of which the initial selection exerts a profound influence on the clustering outcomes. In addition, they usually only co...
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| Vydané v: | Expert systems with applications Ročník 263; s. 125733 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
05.03.2025
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| ISSN: | 0957-4174 |
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| Abstract | Clustering analysis is frequently used in data mining, image processing, artificial intelligence, and so on. Traditional approaches heavily rely on manually configured parameters, of which the initial selection exerts a profound influence on the clustering outcomes. In addition, they usually only consider the relationship between two individual samples when calculating distances, neglecting the overall structure of the dataset, which can negatively affect clustering performance. At the same time, many contemporary algorithms are tailored to specific datasets, posing challenges in achieving optimal clustering performance for intricate, noisy datasets. To address these limitations, we propose an Adaptive Gravitational Clustering Algorithm Integrated with Noise Detection called GCIND. Inspired by the law of gravitation, GCIND takes into account the natural neighborhood structure of the entire dataset, adaptively computing the gravitation between data points by leveraging shared neighbors and Euclidean distance relationships. Our algorithm initially identifies and eliminates outliers or edge points in the dataset. It subsequently uses gravitation to autonomously cluster the remaining core data. Finally, the removed data are reallocated to their respective clusters. GCIND has four notable advantages: (1) it uses gravitation to build the neighborhood graph, reflecting the overall dataset structure; (2) it demonstrates stronger robustness in handling noisy datasets; (3) it uses adaptive gravitational neighborhood graph clustering, removing manual parameter tuning; (4) it adapts to complex manifold-structured datasets, offering broad applicability. Experiments have shown that GCIND, without requiring any parameter settings, demonstrates slightly better performance than the algorithms compared in the study, especially when dealing with complex manifold datasets.
•Uses gravitation to build neighborhood graph, reflecting dataset structure.•Demonstrates strong robustness in handling noisy datasets.•Employs adaptive clustering, removing the need for manual parameter tuning.•Adapts to manifold-structured datasets, offering broad applicability. |
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| AbstractList | Clustering analysis is frequently used in data mining, image processing, artificial intelligence, and so on. Traditional approaches heavily rely on manually configured parameters, of which the initial selection exerts a profound influence on the clustering outcomes. In addition, they usually only consider the relationship between two individual samples when calculating distances, neglecting the overall structure of the dataset, which can negatively affect clustering performance. At the same time, many contemporary algorithms are tailored to specific datasets, posing challenges in achieving optimal clustering performance for intricate, noisy datasets. To address these limitations, we propose an Adaptive Gravitational Clustering Algorithm Integrated with Noise Detection called GCIND. Inspired by the law of gravitation, GCIND takes into account the natural neighborhood structure of the entire dataset, adaptively computing the gravitation between data points by leveraging shared neighbors and Euclidean distance relationships. Our algorithm initially identifies and eliminates outliers or edge points in the dataset. It subsequently uses gravitation to autonomously cluster the remaining core data. Finally, the removed data are reallocated to their respective clusters. GCIND has four notable advantages: (1) it uses gravitation to build the neighborhood graph, reflecting the overall dataset structure; (2) it demonstrates stronger robustness in handling noisy datasets; (3) it uses adaptive gravitational neighborhood graph clustering, removing manual parameter tuning; (4) it adapts to complex manifold-structured datasets, offering broad applicability. Experiments have shown that GCIND, without requiring any parameter settings, demonstrates slightly better performance than the algorithms compared in the study, especially when dealing with complex manifold datasets.
•Uses gravitation to build neighborhood graph, reflecting dataset structure.•Demonstrates strong robustness in handling noisy datasets.•Employs adaptive clustering, removing the need for manual parameter tuning.•Adapts to manifold-structured datasets, offering broad applicability. |
| ArticleNumber | 125733 |
| Author | Wang, Wentong Tang, Dongming Yang, Juntao Liu, Tao Yang, Lijun |
| Author_xml | – sequence: 1 givenname: Juntao orcidid: 0009-0003-0148-5708 surname: Yang fullname: Yang, Juntao email: 2024030059@mail.hfut.edu.cn organization: College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China – sequence: 2 givenname: Lijun surname: Yang fullname: Yang, Lijun email: ylijun@swun.edu.cn organization: College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China – sequence: 3 givenname: Wentong surname: Wang fullname: Wang, Wentong email: 210854002023@stu.swun.edu.cn organization: College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China – sequence: 4 givenname: Tao orcidid: 0000-0002-0348-5977 surname: Liu fullname: Liu, Tao email: tao_liu@swun.edu.cn organization: College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China – sequence: 5 givenname: Dongming orcidid: 0000-0002-6167-1292 surname: Tang fullname: Tang, Dongming email: tdm_2010@swun.edu.cn organization: College of Computer Science and Artificial Intelligence, Southwest Minzu University, Chengdu, China |
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| Cites_doi | 10.1016/j.patrec.2016.05.007 10.1145/37888.37889 10.3390/s22197314 10.5220/0002735003500356 10.1016/j.ins.2023.119479 10.3233/JIFS-202449 10.1109/MIS.2004.1274907 10.1016/j.engappai.2024.108883 10.1007/s10489-022-03661-7 10.1109/TFUZZ.2019.2956900 10.1145/304181.304187 10.1016/j.eswa.2023.120799 10.1016/j.eswa.2022.117927 10.1109/TCYB.2017.2695218 10.1016/j.patcog.2023.109404 10.1007/s10791-008-9066-8 10.1007/s10489-022-03705-y 10.1016/j.patcog.2022.109190 10.1016/j.knosys.2014.03.001 10.1109/CBASE60015.2023.10439072 10.1007/s00521-018-3641-8 10.1016/j.engappai.2022.104743 10.1016/j.ins.2023.120082 10.1016/j.knosys.2015.10.014 10.1109/ACCESS.2022.3198992 10.1126/science.1242072 10.1016/j.engappai.2024.108551 10.1016/j.asoc.2022.109647 10.1016/j.patcog.2023.110063 10.1016/j.eswa.2021.116371 10.1109/TNNLS.2018.2853710 |
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| References | Valero-Mas, Gallego, Alonso-Jiménez, Serra (b29) 2023; 135 Alimohammadi, Nancy Chen (b4) 2022; 191 Rodriguez, Laio (b26) 2014; 344 Huang, Cheng, Zhang (b18) 2023; 231 Cheng, Zhu, Huang, Wu, Yang (b12) 2019; 30 Hinneburg, Keim (b16) 1998 Raeisi, Sesay (b24) 2022; 10 Ankerst, Breunig, Kriegel, Sander (b6) 1999; 28 Zhu, Feng, Huang (b42) 2016; 80 Chen, Yang, Pei, Chen, Du (b9) 2024; 133 Cheng, Huang, Zhang, Xia, Wang, Xie (b10) 2023 Wang, Wu, Huang, Zhang, Nie (b32) 2024; 255 Agrawal, Gehrke, Gunopulos, Raghavan (b2) 1998 Chen, Chen, Liu, Lv, He, Zhang (b8) 2023; 53 Ezugwu, Ikotun, Oyelade, Abualigah, Agushaka, Eke, Akinyelu (b14) 2022; 110 Ren, Sun, Gao, Yu (b25) 2022; 43 Huang, Zhu, Yang, Feng (b19) 2016; 92 Ha, Seok, Lee (b15) 2014; 63 Zhong, Khoshgoftaar, Seliya (b41) 2004; 19 Jin, Wu, Liu, Zhao, Wang (b20) 2023; 647 Zhang, Yang, Zhang (b39) 2021 (pp. 51–56). Amigó, Gonzalo, Artiles, Verdejo (b5) 2009; 12 Visalakshi, S., K. (b30) 2021; 15 Shoaib, Tanveer, Ali, Hayat, Shah (b28) 2024; 659 Dueck (b13) 2009 Zhang, She (b38) 2020 Yang, Yang, Zhang, Liang, Wang, Tang, Liu (b37) 2024; 146 Yang, Xiao (b35) 2024; 136 Zhang, Yang, Zhang, Tang, Liu (b40) 2022; 130 Bache, Lichman (b7) 2013 Kumaravel, Buiatti, Parise, Farella (b21) 2022; 22 Yang, J., Yang, L., Wang, W., & Pu, R. (2023). An Outlier Detection Algorithm based on Local Density and Natural Neighbors. In Albalate, A., Rhinow, S., & Suendermann, D. (2010). A Non-parameterised Hierarchical Pole-based Clustering Algorithm (HPoBC). In Liang, Cai, Yang (b22) 2023; 53 (pp. 350–356). Wang, L.-T., Hoover, N. E., Porter, E. H., & Zasio, J. J. (1987). SSIM: A software levelized compiled-code simulator. In Hu, Liu, Zhang, Fang (b17) 2023; 139 Mau, Huynh (b23) 2021 Cheng, Zhu, Huang, Wu, Yang (b11) 2019; 31 Shirkhorshidi, Wah, Shirkhorshidi, Aghabozorgi (b27) 2021; 29 Abernathy, Celebi (b1) 2022; 207 Wang, Yang, Muntz (b33) 1997 Wang, Yu, Chen, You, Gu, Wong, Zhang (b34) 2018; 48 (pp. 2–8). Agrawal (10.1016/j.eswa.2024.125733_b2) 1998 Zhong (10.1016/j.eswa.2024.125733_b41) 2004; 19 Hu (10.1016/j.eswa.2024.125733_b17) 2023; 139 10.1016/j.eswa.2024.125733_b3 Wang (10.1016/j.eswa.2024.125733_b32) 2024; 255 Kumaravel (10.1016/j.eswa.2024.125733_b21) 2022; 22 Amigó (10.1016/j.eswa.2024.125733_b5) 2009; 12 Cheng (10.1016/j.eswa.2024.125733_b12) 2019; 30 Bache (10.1016/j.eswa.2024.125733_b7) 2013 Zhang (10.1016/j.eswa.2024.125733_b40) 2022; 130 Chen (10.1016/j.eswa.2024.125733_b9) 2024; 133 Cheng (10.1016/j.eswa.2024.125733_b10) 2023 Rodriguez (10.1016/j.eswa.2024.125733_b26) 2014; 344 Zhu (10.1016/j.eswa.2024.125733_b42) 2016; 80 Shirkhorshidi (10.1016/j.eswa.2024.125733_b27) 2021; 29 Zhang (10.1016/j.eswa.2024.125733_b39) 2021 10.1016/j.eswa.2024.125733_b31 Wang (10.1016/j.eswa.2024.125733_b33) 1997 Liang (10.1016/j.eswa.2024.125733_b22) 2023; 53 Ankerst (10.1016/j.eswa.2024.125733_b6) 1999; 28 10.1016/j.eswa.2024.125733_b36 Mau (10.1016/j.eswa.2024.125733_b23) 2021 Huang (10.1016/j.eswa.2024.125733_b19) 2016; 92 Hinneburg (10.1016/j.eswa.2024.125733_b16) 1998 Wang (10.1016/j.eswa.2024.125733_b34) 2018; 48 Abernathy (10.1016/j.eswa.2024.125733_b1) 2022; 207 Visalakshi (10.1016/j.eswa.2024.125733_b30) 2021; 15 Dueck (10.1016/j.eswa.2024.125733_b13) 2009 Cheng (10.1016/j.eswa.2024.125733_b11) 2019; 31 Chen (10.1016/j.eswa.2024.125733_b8) 2023; 53 Jin (10.1016/j.eswa.2024.125733_b20) 2023; 647 Alimohammadi (10.1016/j.eswa.2024.125733_b4) 2022; 191 Valero-Mas (10.1016/j.eswa.2024.125733_b29) 2023; 135 Yang (10.1016/j.eswa.2024.125733_b37) 2024; 146 Ren (10.1016/j.eswa.2024.125733_b25) 2022; 43 Shoaib (10.1016/j.eswa.2024.125733_b28) 2024; 659 Ezugwu (10.1016/j.eswa.2024.125733_b14) 2022; 110 Raeisi (10.1016/j.eswa.2024.125733_b24) 2022; 10 Ha (10.1016/j.eswa.2024.125733_b15) 2014; 63 Huang (10.1016/j.eswa.2024.125733_b18) 2023; 231 Yang (10.1016/j.eswa.2024.125733_b35) 2024; 136 Zhang (10.1016/j.eswa.2024.125733_b38) 2020 |
| References_xml | – volume: 135 year: 2023 ident: b29 article-title: Multilabel prototype generation for data reduction in K-nearest neighbour classification publication-title: Pattern Recognition – volume: 22 start-page: 7314 year: 2022 ident: b21 article-title: Adaptable and robust EEG bad channel detection using local outlier factor (LOF) publication-title: Sensors – start-page: 34:1 year: 2021 end-page: 34:6 ident: b39 publication-title: Robust non-parameter clustering algorithm based on saturated neighborhood graph – volume: 10 start-page: 86286 year: 2022 end-page: 86297 ident: b24 article-title: A distance metric for uneven clusters of unsupervised K-means clustering algorithm publication-title: IEEE Access – volume: 139 year: 2023 ident: b17 article-title: An effective and adaptable K-means algorithm for big data cluster analysis publication-title: Pattern Recognition – volume: 647 year: 2023 ident: b20 article-title: Varying-scale HCA-DBSCAN-based anomaly detection method for multi-dimensional energy data in steel industry publication-title: Information Sciences – volume: 48 start-page: 1383 year: 2018 end-page: 1396 ident: b34 article-title: Clustering by local gravitation publication-title: IEEE Transactions on Cybernetics – reference: (pp. 51–56). – volume: 28 start-page: 49 year: 1999 end-page: 60 ident: b6 article-title: OPTICS: Ordering points to identify the clustering structure publication-title: SIGMOD Record – volume: 19 start-page: 20 year: 2004 end-page: 27 ident: b41 article-title: Analyzing software measurement data with clustering techniques publication-title: IEEE Intelligent Systems – volume: 110 year: 2022 ident: b14 article-title: A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects publication-title: Engineering Applications of Artificial Intelligence – volume: 53 start-page: 3221 year: 2023 end-page: 3239 ident: b22 article-title: Grid-DPC: Improved density peaks clustering based on spatial grid walk publication-title: Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies – volume: 53 start-page: 2506 year: 2023 end-page: 2526 ident: b8 article-title: Parallel gravitational clustering based on grid partitioning for large-scale data publication-title: Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies – start-page: 205 year: 2021 end-page: 217 ident: b23 article-title: Automated attribute weighting fuzzy k-centers algorithm for categorical data clustering publication-title: Lecture notes in computer science – volume: 344 start-page: 1492 year: 2014 end-page: 1496 ident: b26 article-title: Clustering by fast search and find of density peaks publication-title: Science – start-page: 58 year: 1998 end-page: 65 ident: b16 article-title: An efficient approach to clustering in large multimedia databases with noise publication-title: Proceedings of the fourth international conference on knowledge discovery and data mining – volume: 29 start-page: 560 year: 2021 end-page: 568 ident: b27 article-title: Evolving fuzzy clustering approach: An epoch clustering that enables heuristic postpruning publication-title: IEEE Transactions on Fuzzy Systems – reference: (pp. 350–356). – volume: 659 year: 2024 ident: b28 article-title: Grid neighbourhood based three way clustering (3WC) publication-title: Information Sciences – volume: 43 start-page: 21 year: 2022 end-page: 34 ident: b25 article-title: Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy publication-title: Journal of Intelligent & Fuzzy Systems – year: 2009 ident: b13 article-title: Affinity propagation: Clustering data by passing messages – volume: 207 year: 2022 ident: b1 article-title: The incremental online k-means clustering algorithm and its application to color quantization publication-title: Expert Systems with Applications – reference: Wang, L.-T., Hoover, N. E., Porter, E. H., & Zasio, J. J. (1987). SSIM: A software levelized compiled-code simulator. In – volume: 92 start-page: 71 year: 2016 end-page: 77 ident: b19 article-title: A non-parameter outlier detection algorithm based on natural neighbor publication-title: Knowledge-Based Systems – volume: 231 year: 2023 ident: b18 article-title: A novel outlier detecting algorithm based on the outlier turning points publication-title: Expert Systems with Applications – volume: 15 start-page: 1 year: 2021 end-page: 23 ident: b30 article-title: MapReduce-based crow search-adopted partitional clustering algorithms for handling large-scale data publication-title: International Journal of Cognitive Informatics and Natural Intelligence – reference: (pp. 2–8). – volume: 31 start-page: 8051 year: 2019 end-page: 8068 ident: b11 article-title: A local cores-based hierarchical clustering algorithm for data sets with complex structures publication-title: Neural Computing and Applications – volume: 80 start-page: 30 year: 2016 end-page: 36 ident: b42 article-title: Natural neighbor: A self-adaptive neighborhood method without parameter k publication-title: Pattern Recognition – reference: Albalate, A., Rhinow, S., & Suendermann, D. (2010). A Non-parameterised Hierarchical Pole-based Clustering Algorithm (HPoBC). In – start-page: 186 year: 1997 end-page: 195 ident: b33 article-title: STING: a statistical information grid approach to spatial data mining – volume: 130 year: 2022 ident: b40 article-title: Non-parameter clustering algorithm based on saturated neighborhood graph publication-title: Applied Soft Computing – volume: 63 start-page: 15 year: 2014 end-page: 23 ident: b15 article-title: Robust outlier detection using the instability factor publication-title: Knowledge-Based Systems – volume: 146 year: 2024 ident: b37 article-title: GNaN: A natural neighbor search algorithm based on universal gravitation publication-title: Pattern Recognition – start-page: 1 year: 2023 end-page: 14 ident: b10 article-title: K-means clustering with natural density peaks for discovering arbitrary-shaped clusters publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 255 year: 2024 ident: b32 article-title: Projected fuzzy c-means clustering algorithm with instance penalty publication-title: Expert Systems with Applications – start-page: 94 year: 1998 end-page: 105 ident: b2 article-title: Automatic subspace clustering of high dimensional data for data mining applications publication-title: SIGMOD 1998, proceedings ACM SIGMOD international conference on management of data, June 2-4, 1998, seattle, washington, USA – volume: 191 year: 2022 ident: b4 article-title: Performance evaluation of outlier detection techniques in production timeseries: A systematic review and meta-analysis publication-title: Expert Systems with Applications – volume: 30 start-page: 985 year: 2019 end-page: 999 ident: b12 article-title: A novel cluster validity index based on local cores publication-title: IEEE Transactions on Neural Networks Learning Systems – reference: Yang, J., Yang, L., Wang, W., & Pu, R. (2023). An Outlier Detection Algorithm based on Local Density and Natural Neighbors. In – volume: 12 start-page: 461 year: 2009 end-page: 486 ident: b5 article-title: A comparison of extrinsic clustering evaluation metrics based on formal constraints publication-title: Information Retrieval – year: 2013 ident: b7 article-title: UCI machine learning repository – volume: 133 year: 2024 ident: b9 article-title: A simple rapid sample-based clustering for large-scale data publication-title: Engineering Applications of Artificial Intelligence – volume: 136 year: 2024 ident: b35 article-title: An improved density peaks clustering algorithm based on the generalized neighbors similarity publication-title: Engineering Applications of Artificial Intelligence – year: 2020 ident: b38 article-title: A novel hierarchical clustering approach based on universal gravitation publication-title: Mathematical Problems in Engineering – volume: 15 start-page: 1 year: 2021 ident: 10.1016/j.eswa.2024.125733_b30 article-title: MapReduce-based crow search-adopted partitional clustering algorithms for handling large-scale data publication-title: International Journal of Cognitive Informatics and Natural Intelligence – year: 2020 ident: 10.1016/j.eswa.2024.125733_b38 article-title: A novel hierarchical clustering approach based on universal gravitation publication-title: Mathematical Problems in Engineering – volume: 80 start-page: 30 year: 2016 ident: 10.1016/j.eswa.2024.125733_b42 article-title: Natural neighbor: A self-adaptive neighborhood method without parameter k publication-title: Pattern Recognition doi: 10.1016/j.patrec.2016.05.007 – ident: 10.1016/j.eswa.2024.125733_b31 doi: 10.1145/37888.37889 – volume: 22 start-page: 7314 year: 2022 ident: 10.1016/j.eswa.2024.125733_b21 article-title: Adaptable and robust EEG bad channel detection using local outlier factor (LOF) publication-title: Sensors doi: 10.3390/s22197314 – ident: 10.1016/j.eswa.2024.125733_b3 doi: 10.5220/0002735003500356 – volume: 647 year: 2023 ident: 10.1016/j.eswa.2024.125733_b20 article-title: Varying-scale HCA-DBSCAN-based anomaly detection method for multi-dimensional energy data in steel industry publication-title: Information Sciences doi: 10.1016/j.ins.2023.119479 – volume: 43 start-page: 21 year: 2022 ident: 10.1016/j.eswa.2024.125733_b25 article-title: Density peaks clustering based on local fair density and fuzzy k-nearest neighbors membership allocation strategy publication-title: Journal of Intelligent & Fuzzy Systems doi: 10.3233/JIFS-202449 – volume: 19 start-page: 20 year: 2004 ident: 10.1016/j.eswa.2024.125733_b41 article-title: Analyzing software measurement data with clustering techniques publication-title: IEEE Intelligent Systems doi: 10.1109/MIS.2004.1274907 – volume: 136 year: 2024 ident: 10.1016/j.eswa.2024.125733_b35 article-title: An improved density peaks clustering algorithm based on the generalized neighbors similarity publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2024.108883 – start-page: 34:1 year: 2021 ident: 10.1016/j.eswa.2024.125733_b39 – volume: 53 start-page: 2506 year: 2023 ident: 10.1016/j.eswa.2024.125733_b8 article-title: Parallel gravitational clustering based on grid partitioning for large-scale data publication-title: Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies doi: 10.1007/s10489-022-03661-7 – volume: 29 start-page: 560 year: 2021 ident: 10.1016/j.eswa.2024.125733_b27 article-title: Evolving fuzzy clustering approach: An epoch clustering that enables heuristic postpruning publication-title: IEEE Transactions on Fuzzy Systems doi: 10.1109/TFUZZ.2019.2956900 – volume: 28 start-page: 49 year: 1999 ident: 10.1016/j.eswa.2024.125733_b6 article-title: OPTICS: Ordering points to identify the clustering structure publication-title: SIGMOD Record doi: 10.1145/304181.304187 – volume: 231 year: 2023 ident: 10.1016/j.eswa.2024.125733_b18 article-title: A novel outlier detecting algorithm based on the outlier turning points publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.120799 – volume: 255 year: 2024 ident: 10.1016/j.eswa.2024.125733_b32 article-title: Projected fuzzy c-means clustering algorithm with instance penalty publication-title: Expert Systems with Applications – start-page: 186 year: 1997 ident: 10.1016/j.eswa.2024.125733_b33 article-title: STING: a statistical information grid approach to spatial data mining – start-page: 1 year: 2023 ident: 10.1016/j.eswa.2024.125733_b10 article-title: K-means clustering with natural density peaks for discovering arbitrary-shaped clusters publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 207 year: 2022 ident: 10.1016/j.eswa.2024.125733_b1 article-title: The incremental online k-means clustering algorithm and its application to color quantization publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.117927 – volume: 48 start-page: 1383 year: 2018 ident: 10.1016/j.eswa.2024.125733_b34 article-title: Clustering by local gravitation publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2017.2695218 – volume: 139 year: 2023 ident: 10.1016/j.eswa.2024.125733_b17 article-title: An effective and adaptable K-means algorithm for big data cluster analysis publication-title: Pattern Recognition doi: 10.1016/j.patcog.2023.109404 – volume: 12 start-page: 461 year: 2009 ident: 10.1016/j.eswa.2024.125733_b5 article-title: A comparison of extrinsic clustering evaluation metrics based on formal constraints publication-title: Information Retrieval doi: 10.1007/s10791-008-9066-8 – year: 2013 ident: 10.1016/j.eswa.2024.125733_b7 – year: 2009 ident: 10.1016/j.eswa.2024.125733_b13 – volume: 53 start-page: 3221 year: 2023 ident: 10.1016/j.eswa.2024.125733_b22 article-title: Grid-DPC: Improved density peaks clustering based on spatial grid walk publication-title: Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies doi: 10.1007/s10489-022-03705-y – volume: 135 year: 2023 ident: 10.1016/j.eswa.2024.125733_b29 article-title: Multilabel prototype generation for data reduction in K-nearest neighbour classification publication-title: Pattern Recognition doi: 10.1016/j.patcog.2022.109190 – volume: 63 start-page: 15 year: 2014 ident: 10.1016/j.eswa.2024.125733_b15 article-title: Robust outlier detection using the instability factor publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2014.03.001 – start-page: 94 year: 1998 ident: 10.1016/j.eswa.2024.125733_b2 article-title: Automatic subspace clustering of high dimensional data for data mining applications – ident: 10.1016/j.eswa.2024.125733_b36 doi: 10.1109/CBASE60015.2023.10439072 – volume: 31 start-page: 8051 year: 2019 ident: 10.1016/j.eswa.2024.125733_b11 article-title: A local cores-based hierarchical clustering algorithm for data sets with complex structures publication-title: Neural Computing and Applications doi: 10.1007/s00521-018-3641-8 – volume: 110 year: 2022 ident: 10.1016/j.eswa.2024.125733_b14 article-title: A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2022.104743 – start-page: 205 year: 2021 ident: 10.1016/j.eswa.2024.125733_b23 article-title: Automated attribute weighting fuzzy k-centers algorithm for categorical data clustering – volume: 659 year: 2024 ident: 10.1016/j.eswa.2024.125733_b28 article-title: Grid neighbourhood based three way clustering (3WC) publication-title: Information Sciences doi: 10.1016/j.ins.2023.120082 – volume: 92 start-page: 71 year: 2016 ident: 10.1016/j.eswa.2024.125733_b19 article-title: A non-parameter outlier detection algorithm based on natural neighbor publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.10.014 – start-page: 58 year: 1998 ident: 10.1016/j.eswa.2024.125733_b16 article-title: An efficient approach to clustering in large multimedia databases with noise – volume: 10 start-page: 86286 year: 2022 ident: 10.1016/j.eswa.2024.125733_b24 article-title: A distance metric for uneven clusters of unsupervised K-means clustering algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3198992 – volume: 344 start-page: 1492 year: 2014 ident: 10.1016/j.eswa.2024.125733_b26 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 133 year: 2024 ident: 10.1016/j.eswa.2024.125733_b9 article-title: A simple rapid sample-based clustering for large-scale data publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2024.108551 – volume: 130 year: 2022 ident: 10.1016/j.eswa.2024.125733_b40 article-title: Non-parameter clustering algorithm based on saturated neighborhood graph publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2022.109647 – volume: 146 year: 2024 ident: 10.1016/j.eswa.2024.125733_b37 article-title: GNaN: A natural neighbor search algorithm based on universal gravitation publication-title: Pattern Recognition doi: 10.1016/j.patcog.2023.110063 – volume: 191 year: 2022 ident: 10.1016/j.eswa.2024.125733_b4 article-title: Performance evaluation of outlier detection techniques in production timeseries: A systematic review and meta-analysis publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116371 – volume: 30 start-page: 985 year: 2019 ident: 10.1016/j.eswa.2024.125733_b12 article-title: A novel cluster validity index based on local cores publication-title: IEEE Transactions on Neural Networks Learning Systems doi: 10.1109/TNNLS.2018.2853710 |
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