A Sampling-Based Density Peaks Clustering Algorithm for Large-Scale Data
•An improved triangle-inequality-based search strategy is proposed.•An approximate local density calculation of representatives is proposed.•Experiments show that our algorithm costs far less time than DPC and other state-of-the-art algorithms proposed recently. With the rapid development of informa...
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| Vydáno v: | Pattern recognition Ročník 136; s. 109238 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.04.2023
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | •An improved triangle-inequality-based search strategy is proposed.•An approximate local density calculation of representatives is proposed.•Experiments show that our algorithm costs far less time than DPC and other state-of-the-art algorithms proposed recently.
With the rapid development of information technology, massive amount of data is generated. How to discover useful information to support decision-making has become one of the focuses of scholar's research. Clustering is thought to be one of the main means to deal with large-scale data. Density peaks clustering (DPC) is an effective density-based clustering algorithm which is widely applied in numerous fields because of its satisfactory performance. However, the computational complexity of DPC is O(N2) which is not friendly to large-scale data. To solve this issue, a sampling-based density peaks clustering algorithm for large-scale data (SDPC) is proposed. Firstly, a sampling method is used to reduce the distance calculations. Secondly, approximate representatives are identified by an improved TI search strategy which further accelerates the clustering process. Afterwards, the approximate representatives are clustered by DPC. Finally, the remaining points are allocated to the same cluster as its nearest representatives. Experimental results on both synthetic datasets and real-world datasets illustrate that SDPC is more efficient than DPC, while its clustering performance maintains the same level as DPC. |
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| AbstractList | •An improved triangle-inequality-based search strategy is proposed.•An approximate local density calculation of representatives is proposed.•Experiments show that our algorithm costs far less time than DPC and other state-of-the-art algorithms proposed recently.
With the rapid development of information technology, massive amount of data is generated. How to discover useful information to support decision-making has become one of the focuses of scholar's research. Clustering is thought to be one of the main means to deal with large-scale data. Density peaks clustering (DPC) is an effective density-based clustering algorithm which is widely applied in numerous fields because of its satisfactory performance. However, the computational complexity of DPC is O(N2) which is not friendly to large-scale data. To solve this issue, a sampling-based density peaks clustering algorithm for large-scale data (SDPC) is proposed. Firstly, a sampling method is used to reduce the distance calculations. Secondly, approximate representatives are identified by an improved TI search strategy which further accelerates the clustering process. Afterwards, the approximate representatives are clustered by DPC. Finally, the remaining points are allocated to the same cluster as its nearest representatives. Experimental results on both synthetic datasets and real-world datasets illustrate that SDPC is more efficient than DPC, while its clustering performance maintains the same level as DPC. |
| ArticleNumber | 109238 |
| Author | Shi, Tianhao Xu, Xiao Ding, Shifei Zhang, Jian Li, Chao Ding, Ling Guo, Lili |
| Author_xml | – sequence: 1 givenname: Shifei surname: Ding fullname: Ding, Shifei organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 2 givenname: Chao surname: Li fullname: Li, Chao organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 3 givenname: Xiao surname: Xu fullname: Xu, Xiao email: xu_xiao@cumt.edu.cn organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 4 givenname: Ling surname: Ding fullname: Ding, Ling email: dingsf@cumt.edu.cn, 414211048@qq.com organization: College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China – sequence: 5 givenname: Jian surname: Zhang fullname: Zhang, Jian organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 6 givenname: Lili surname: Guo fullname: Guo, Lili organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China – sequence: 7 givenname: Tianhao surname: Shi fullname: Shi, Tianhao email: 2470486977@qq.com organization: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China |
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| Cites_doi | 10.1016/j.patcog.2022.108809 10.1007/s10489-021-02278-6 10.1007/s00500-019-04365-w 10.1016/j.patcog.2021.108041 10.1016/j.knosys.2018.09.007 10.1016/j.patcog.2018.05.030 10.1109/TII.2016.2628747 10.1109/TKDE.2016.2609423 10.1016/j.patcog.2022.108745 10.1016/j.eswa.2019.01.074 10.1016/j.patcog.2020.107449 10.1016/j.knosys.2017.07.027 10.1109/TKDE.2019.2903410 10.1007/s13042-017-0648-x 10.1007/s10115-018-1189-7 10.1016/j.ins.2020.08.052 10.1007/s00500-018-3183-0 10.1016/j.ins.2020.11.050 10.1016/j.knosys.2020.106028 10.1109/TKDE.2015.2460735 10.1007/s13042-016-0603-2 10.1007/s10489-020-01926-7 10.1016/j.knosys.2016.02.001 10.1016/j.eswa.2018.07.075 10.1016/j.neucom.2021.05.071 10.1016/j.knosys.2019.06.032 10.1016/j.ins.2019.09.001 10.1016/j.knosys.2019.105088 10.1126/science.1242072 10.1016/j.knosys.2018.05.034 10.1016/j.patcog.2020.107554 10.1109/TNNLS.2019.2909425 10.1016/j.patcog.2020.107452 |
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| References | Chen, Hu, Fan (bib0022) 2020; 187 Xu, Ding, Xu (bib0013) 2019; 23 189 (2020), 105088. DOI: 10.1016/j.knosys.2019.105088. Wu, Wilamowski (bib0024) 2017; 13 Almalawi, Fahad, Tari (bib0035) 2016; 28 Bai, Cheng, Liang (bib0025) 2017; 13 Hou, Zhang (bib0030) 2020; 108 Abbas, El-Zoghabi (bib0032) 2020; 109 Qv, Ma, Tong (bib0002) 2022; 129 Xu, Ding, Wang (bib0014) 2020; 200 Xu, Ding, Wang (bib0028) 2021; 554 Xu, Ding, Shi (bib0021) 2018; 158 Zhao, Liang, Dang (bib0033) 2019; 163 Du, Ding, Xue (bib0018) 2019; 59 Wang, Chen, Nie (bib0004) 2022; 130 Arthur, Vassilvitskil (bib0037) 2007 Laohakiat, Sa-ing (bib0008) 2021; 547 Baek, Yoon, Song (bib0003) 2021; 118 Ding, Du, Sun (bib0012) 2017; 133 Zhang, Chen, Yu (bib0026) 2016; 28 Unlu, Xanthopoulos (bib0005) 2019; 125 Liu, Li, Du (bib0027) 2017; 107 Khalili, Dakhilalian, Susilo (bib0006) 2020; 510 Zhang, Ding, Wang (bib0007) 2021; 51 Rodriguez, Laio (bib0011) 2014; 334 Du, Ding, Xu (bib0015) 2018; 9 Du, Ding, Jia (bib0016) 2016; 99 Huang, Wang, Wu (bib0034) 2020; 32 Shi, Ding, Xu (bib0019) 2021; 51 Seyedi, Lotfi, Moradi (bib0017) 2019; 115 Guo, Yang, Chen (bib0010) 2020; 24 Chen, Tang, Bouguila (bib0009) 2018; 83 Fang, Qiu (bib0029) 2020; 107 Pan Y, Pan Z, Wang Y, et al., A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy. Lotfi, Moradi (bib0031) 2020; 107 Guan, Li, He (bib0020) 2021; 445 Chen, Chen, Wu (bib0001) 2020; 31 Xu, Ding, Du (bib0023) 2018; 9 Xu (10.1016/j.patcog.2022.109238_bib0023) 2018; 9 Du (10.1016/j.patcog.2022.109238_bib0015) 2018; 9 Fang (10.1016/j.patcog.2022.109238_bib0029) 2020; 107 Huang (10.1016/j.patcog.2022.109238_bib0034) 2020; 32 Xu (10.1016/j.patcog.2022.109238_bib0021) 2018; 158 10.1016/j.patcog.2022.109238_bib0036 Chen (10.1016/j.patcog.2022.109238_bib0001) 2020; 31 Wang (10.1016/j.patcog.2022.109238_bib0004) 2022; 130 Zhang (10.1016/j.patcog.2022.109238_bib0026) 2016; 28 Baek (10.1016/j.patcog.2022.109238_bib0003) 2021; 118 Laohakiat (10.1016/j.patcog.2022.109238_bib0008) 2021; 547 Guo (10.1016/j.patcog.2022.109238_bib0010) 2020; 24 Xu (10.1016/j.patcog.2022.109238_bib0013) 2019; 23 Du (10.1016/j.patcog.2022.109238_bib0016) 2016; 99 Khalili (10.1016/j.patcog.2022.109238_bib0006) 2020; 510 Rodriguez (10.1016/j.patcog.2022.109238_bib0011) 2014; 334 Ding (10.1016/j.patcog.2022.109238_bib0012) 2017; 133 Almalawi (10.1016/j.patcog.2022.109238_bib0035) 2016; 28 Du (10.1016/j.patcog.2022.109238_bib0018) 2019; 59 Chen (10.1016/j.patcog.2022.109238_bib0022) 2020; 187 Abbas (10.1016/j.patcog.2022.109238_bib0032) 2020; 109 Unlu (10.1016/j.patcog.2022.109238_bib0005) 2019; 125 Wu (10.1016/j.patcog.2022.109238_bib0024) 2017; 13 Shi (10.1016/j.patcog.2022.109238_bib0019) 2021; 51 Zhang (10.1016/j.patcog.2022.109238_bib0007) 2021; 51 Seyedi (10.1016/j.patcog.2022.109238_bib0017) 2019; 115 Lotfi (10.1016/j.patcog.2022.109238_bib0031) 2020; 107 Zhao (10.1016/j.patcog.2022.109238_bib0033) 2019; 163 Arthur (10.1016/j.patcog.2022.109238_bib0037) 2007 Bai (10.1016/j.patcog.2022.109238_bib0025) 2017; 13 Liu (10.1016/j.patcog.2022.109238_bib0027) 2017; 107 Guan (10.1016/j.patcog.2022.109238_bib0020) 2021; 445 Hou (10.1016/j.patcog.2022.109238_bib0030) 2020; 108 Xu (10.1016/j.patcog.2022.109238_bib0014) 2020; 200 Chen (10.1016/j.patcog.2022.109238_bib0009) 2018; 83 Qv (10.1016/j.patcog.2022.109238_bib0002) 2022; 129 Xu (10.1016/j.patcog.2022.109238_bib0028) 2021; 554 |
| References_xml | – volume: 547 start-page: 404 year: 2021 end-page: 426 ident: bib0008 article-title: An incremental density-based clustering framework using fuzzy local clustering publication-title: Inf. Sci. – volume: 187 year: 2020 ident: bib0022 article-title: Fast density peak clustering for large scale data based on kNN publication-title: Knowledge-Based Syst – volume: 107 start-page: 442 year: 2017 end-page: 447 ident: bib0027 article-title: Parallel implementation of density peaks clustering algorithm based on spark publication-title: 7 – volume: 13 start-page: 1620 year: 2017 end-page: 1628 ident: bib0024 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inform. – volume: 108 year: 2020 ident: bib0030 article-title: Density peaks clustering based on relative density relationship publication-title: Pattern Recognit – volume: 107 year: 2020 ident: bib0031 article-title: Density peaks clustering based on density backbone and fuzzy neighborhood publication-title: Pattern Recognit – volume: 130 year: 2022 ident: bib0004 article-title: Directly solving normalized cut for multi-view data publication-title: Pattern Recognit – volume: 445 start-page: 401 year: 2021 end-page: 418 ident: bib0020 article-title: Fast hierarchical clustering of local density peaks via an association degree transfer method publication-title: Neurocomputing – volume: 31 start-page: 725 year: 2020 end-page: 736 ident: bib0001 article-title: LABIN: balanced min cut for large-scale data publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 32 start-page: 1212 year: 2020 end-page: 1226 ident: bib0034 article-title: Ultra-scalable spectral clustering and ensemble clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 24 start-page: 7395 year: 2020 end-page: 7415 ident: bib0010 article-title: Grid-based dynamic robust multi-objective brain storm optimization algorithm publication-title: Soft Comput – volume: 163 start-page: 416 year: 2019 end-page: 428 ident: bib0033 article-title: A stratified sampling based clustering algorithm for large-scale data publication-title: Knowledge-Based Syst – volume: 510 start-page: 155 year: 2020 end-page: 164 ident: bib0006 article-title: Efficient chameleon hash functions in the enhanced collision resistant model publication-title: Inf. Sci. – volume: 125 start-page: 33 year: 2019 end-page: 39 ident: bib0005 article-title: Estimating the number of clusters in a dataset via consensus clustering publication-title: Expert Syst. Appl. – volume: 99 start-page: 135 year: 2016 end-page: 145 ident: bib0016 article-title: Study on density peaks clustering based on k-nearest neighbors and principal component analysis publication-title: Knowledge-Based Syst – reference: , 189 (2020), 105088. DOI: 10.1016/j.knosys.2019.105088. – volume: 9 start-page: 743 year: 2018 end-page: 754 ident: bib0023 article-title: GDCG: an efficient density peak clustering algorithm based on grid publication-title: Int. J. Mach. Learn. Cybern. – volume: 28 start-page: 68 year: 2016 end-page: 81 ident: bib0035 article-title: kNNVWC: an efficient k-nearest neighbors approach based on various-widths clustering publication-title: IEEE Trans. Knowl. Data Eng. – volume: 51 start-page: 7917 year: 2021 end-page: 7932 ident: bib0019 article-title: A community detection algorithm based on Quasi-Laplacian centrality peaks clustering publication-title: Appl. Intell. – volume: 334 start-page: 1492 year: 2014 end-page: 1496 ident: bib0011 article-title: Clustering by fast search and find of density peaks publication-title: Science – volume: 200 start-page: 1 year: 2020 end-page: 11 ident: bib0014 article-title: A robust density peaks clustering algorithm with density-sensitive similarity publication-title: Knowledge-Based Syst – volume: 28 start-page: 3218 year: 2016 end-page: 3230 ident: bib0026 article-title: Efficient distributed density peaks for clustering large data sets in mapreduce publication-title: IEEE Trans. Knowl. Data Eng. – volume: 115 start-page: 314 year: 2019 end-page: 328 ident: bib0017 article-title: Dynamic graph-based label propagation for density peaks clustering publication-title: Expert Syst. Appl. – reference: Pan Y, Pan Z, Wang Y, et al., A new fast search algorithm for exact k-nearest neighbors based on optimal triangle-inequality-based check strategy. – volume: 59 start-page: 285 year: 2019 end-page: 309 ident: bib0018 article-title: A novel density peaks clustering with sensitivity of local density and density-adaptive metric publication-title: Knowl. Inf. Syst. – volume: 129 year: 2022 ident: bib0002 article-title: Clustering by centroid drift and boundary shrinkage publication-title: Pattern Recognit – volume: 23 start-page: 5171 year: 2019 end-page: 5183 ident: bib0013 article-title: A feasible density peaks clustering algorithm with a merging strategy publication-title: Soft Comput – volume: 554 start-page: 61 year: 2021 end-page: 83 ident: bib0028 article-title: A fast density peaks clustering algorithm with sparse search publication-title: Inf. Sci. – volume: 83 start-page: 375 year: 2018 end-page: 387 ident: bib0009 article-title: A fast clustering algorithm based on pruning unnecessary distance computations in dbscan for high-dimensional data publication-title: Pattern Recognit – volume: 9 start-page: 1335 year: 2018 end-page: 1349 ident: bib0015 article-title: Density peaks clustering using geodesic distances publication-title: Int. J. March. Learn. Cybern. – volume: 118 year: 2021 ident: bib0003 article-title: Deep self-representative subspace clustering network publication-title: Pattern Recognit – volume: 109 year: 2020 ident: bib0032 article-title: DenMune: Density peak based clustering using mutual nearest neighbors publication-title: Pattern Recognit – volume: 158 start-page: 65 year: 2018 end-page: 74 ident: bib0021 article-title: An improved density peaks clustering algorithm with fast finding cluster centers publication-title: Knowledge-Based Syst – volume: 107 year: 2020 ident: bib0029 article-title: Adaptive core fusion-based density peaks clustering for complex data with arbitrary shapes and densities publication-title: Pattern Recognit – volume: 51 start-page: 2031 year: 2021 end-page: 2044 ident: bib0007 article-title: Chameleon algorithm based on mutual K-nearest neighbors publication-title: Appl. Intell. – volume: 13 start-page: 1620 year: 2017 end-page: 1628 ident: bib0025 article-title: Fast density clustering strategies based on the k-means algorithm publication-title: Pattern Recognit – volume: 133 start-page: 294 year: 2017 end-page: 313 ident: bib0012 article-title: An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood publication-title: Knowledge-Based Syst – start-page: 1027 year: 2007 end-page: 1035 ident: bib0037 article-title: k-means++: The advantages of careful seeding publication-title: 18 – volume: 130 year: 2022 ident: 10.1016/j.patcog.2022.109238_bib0004 article-title: Directly solving normalized cut for multi-view data publication-title: Pattern Recognit doi: 10.1016/j.patcog.2022.108809 – volume: 107 start-page: 442 year: 2017 ident: 10.1016/j.patcog.2022.109238_bib0027 article-title: Parallel implementation of density peaks clustering algorithm based on spark publication-title: 7th ICICT – volume: 51 start-page: 7917 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0019 article-title: A community detection algorithm based on Quasi-Laplacian centrality peaks clustering publication-title: Appl. Intell. doi: 10.1007/s10489-021-02278-6 – volume: 24 start-page: 7395 issue: 10 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0010 article-title: Grid-based dynamic robust multi-objective brain storm optimization algorithm publication-title: Soft Comput doi: 10.1007/s00500-019-04365-w – volume: 118 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0003 article-title: Deep self-representative subspace clustering network publication-title: Pattern Recognit doi: 10.1016/j.patcog.2021.108041 – volume: 109 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0032 article-title: DenMune: Density peak based clustering using mutual nearest neighbors publication-title: Pattern Recognit – volume: 163 start-page: 416 year: 2019 ident: 10.1016/j.patcog.2022.109238_bib0033 article-title: A stratified sampling based clustering algorithm for large-scale data publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2018.09.007 – volume: 83 start-page: 375 year: 2018 ident: 10.1016/j.patcog.2022.109238_bib0009 article-title: A fast clustering algorithm based on pruning unnecessary distance computations in dbscan for high-dimensional data publication-title: Pattern Recognit doi: 10.1016/j.patcog.2018.05.030 – volume: 13 start-page: 1620 issue: 4 year: 2017 ident: 10.1016/j.patcog.2022.109238_bib0024 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2016.2628747 – volume: 28 start-page: 3218 issue: 12 year: 2016 ident: 10.1016/j.patcog.2022.109238_bib0026 article-title: Efficient distributed density peaks for clustering large data sets in mapreduce publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2016.2609423 – volume: 129 year: 2022 ident: 10.1016/j.patcog.2022.109238_bib0002 article-title: Clustering by centroid drift and boundary shrinkage publication-title: Pattern Recognit doi: 10.1016/j.patcog.2022.108745 – volume: 125 start-page: 33 year: 2019 ident: 10.1016/j.patcog.2022.109238_bib0005 article-title: Estimating the number of clusters in a dataset via consensus clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.01.074 – volume: 107 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0031 article-title: Density peaks clustering based on density backbone and fuzzy neighborhood publication-title: Pattern Recognit doi: 10.1016/j.patcog.2020.107449 – volume: 133 start-page: 294 year: 2017 ident: 10.1016/j.patcog.2022.109238_bib0012 article-title: An entropy-based density peaks clustering algorithm for mixed type data employing fuzzy neighborhood publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2017.07.027 – volume: 32 start-page: 1212 issue: 6 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0034 article-title: Ultra-scalable spectral clustering and ensemble clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2019.2903410 – volume: 9 start-page: 1335 issue: 8 year: 2018 ident: 10.1016/j.patcog.2022.109238_bib0015 article-title: Density peaks clustering using geodesic distances publication-title: Int. J. March. Learn. Cybern. doi: 10.1007/s13042-017-0648-x – volume: 59 start-page: 285 issue: 2 year: 2019 ident: 10.1016/j.patcog.2022.109238_bib0018 article-title: A novel density peaks clustering with sensitivity of local density and density-adaptive metric publication-title: Knowl. Inf. Syst. doi: 10.1007/s10115-018-1189-7 – volume: 547 start-page: 404 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0008 article-title: An incremental density-based clustering framework using fuzzy local clustering publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.08.052 – volume: 23 start-page: 5171 issue: 13 year: 2019 ident: 10.1016/j.patcog.2022.109238_bib0013 article-title: A feasible density peaks clustering algorithm with a merging strategy publication-title: Soft Comput doi: 10.1007/s00500-018-3183-0 – volume: 554 start-page: 61 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0028 article-title: A fast density peaks clustering algorithm with sparse search publication-title: Inf. Sci. doi: 10.1016/j.ins.2020.11.050 – volume: 200 start-page: 1 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0014 article-title: A robust density peaks clustering algorithm with density-sensitive similarity publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2020.106028 – volume: 28 start-page: 68 issue: 1 year: 2016 ident: 10.1016/j.patcog.2022.109238_bib0035 article-title: kNNVWC: an efficient k-nearest neighbors approach based on various-widths clustering publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2015.2460735 – volume: 9 start-page: 743 issue: 5 year: 2018 ident: 10.1016/j.patcog.2022.109238_bib0023 article-title: GDCG: an efficient density peak clustering algorithm based on grid publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-016-0603-2 – volume: 51 start-page: 2031 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0007 article-title: Chameleon algorithm based on mutual K-nearest neighbors publication-title: Appl. Intell. doi: 10.1007/s10489-020-01926-7 – volume: 99 start-page: 135 year: 2016 ident: 10.1016/j.patcog.2022.109238_bib0016 article-title: Study on density peaks clustering based on k-nearest neighbors and principal component analysis publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2016.02.001 – volume: 115 start-page: 314 year: 2019 ident: 10.1016/j.patcog.2022.109238_bib0017 article-title: Dynamic graph-based label propagation for density peaks clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2018.07.075 – volume: 13 start-page: 1620 issue: 4 year: 2017 ident: 10.1016/j.patcog.2022.109238_bib0025 article-title: Fast density clustering strategies based on the k-means algorithm publication-title: Pattern Recognit – volume: 445 start-page: 401 year: 2021 ident: 10.1016/j.patcog.2022.109238_bib0020 article-title: Fast hierarchical clustering of local density peaks via an association degree transfer method publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.05.071 – volume: 187 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0022 article-title: Fast density peak clustering for large scale data based on kNN publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2019.06.032 – volume: 510 start-page: 155 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0006 article-title: Efficient chameleon hash functions in the enhanced collision resistant model publication-title: Inf. Sci. doi: 10.1016/j.ins.2019.09.001 – ident: 10.1016/j.patcog.2022.109238_bib0036 doi: 10.1016/j.knosys.2019.105088 – volume: 334 start-page: 1492 issue: 6191 year: 2014 ident: 10.1016/j.patcog.2022.109238_bib0011 article-title: Clustering by fast search and find of density peaks publication-title: Science doi: 10.1126/science.1242072 – volume: 158 start-page: 65 year: 2018 ident: 10.1016/j.patcog.2022.109238_bib0021 article-title: An improved density peaks clustering algorithm with fast finding cluster centers publication-title: Knowledge-Based Syst doi: 10.1016/j.knosys.2018.05.034 – volume: 108 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0030 article-title: Density peaks clustering based on relative density relationship publication-title: Pattern Recognit doi: 10.1016/j.patcog.2020.107554 – volume: 31 start-page: 725 issue: 3 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0001 article-title: LABIN: balanced min cut for large-scale data publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2019.2909425 – start-page: 1027 year: 2007 ident: 10.1016/j.patcog.2022.109238_bib0037 article-title: k-means++: The advantages of careful seeding – volume: 107 year: 2020 ident: 10.1016/j.patcog.2022.109238_bib0029 article-title: Adaptive core fusion-based density peaks clustering for complex data with arbitrary shapes and densities publication-title: Pattern Recognit doi: 10.1016/j.patcog.2020.107452 |
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