Density-sensitive fuzzy kernel maximum entropy clustering algorithm
Maximum entropy clustering algorithm (ME) has lately received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that different clusters obtained by traditional ME tend to converge to the same one duri...
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| Published in: | Knowledge-based systems Vol. 166; pp. 42 - 57 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Amsterdam
Elsevier B.V
15.02.2019
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Maximum entropy clustering algorithm (ME) has lately received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that different clusters obtained by traditional ME tend to converge to the same one during its process of iteration affected by regularization coefficient and these cluster centers are subject to bias due to its sensitivity to different distributions of objects. These drawbacks of traditional ME can result in its failure of revealing the natural groupings in most datasets, especially in non-Gaussian distributed datasets. In order to address those limitations, we present a novel density-sensitive fuzzy kernel maximum entropy clustering algorithm in this paper. In the proposed approach, to accommodate non-Gaussian distributed cases, the dataset to be clustered in the original space is firstly implicitly mapped into high-dimensional feature space through the kernel function. By introducing the kernel function-based similarity terms in the update formula of the cluster centers, the effect of the objects not belonging to the current cluster on the update of its corresponding center can be counteracted, and simultaneously the influence of regularization coefficient on the clustering result is restricted as well, which can effectively overcome the convergence of the different clusters encountered by traditional ME. In addition, in order to prevent cluster centers from biases caused by the different distribution of the objects in the feature space, the relative density-based weights are also incorporated into the cost function, which can help the proposed approach produce more reasonable and accurate clustering results. In the experiments, the influence of the different parameters on the clustering performance is discussed in detail and some suggestions are also provided. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing clustering techniques with good robustness. |
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| AbstractList | Maximum entropy clustering algorithm (ME) has lately received great attention for its high performance in large-scale data clustering and simplicity in implementation. However, previous studies have demonstrated that different clusters obtained by traditional ME tend to converge to the same one during its process of iteration affected by regularization coefficient and these cluster centers are subject to bias due to its sensitivity to different distributions of objects. These drawbacks of traditional ME can result in its failure of revealing the natural groupings in most datasets, especially in non-Gaussian distributed datasets. In order to address those limitations, we present a novel density-sensitive fuzzy kernel maximum entropy clustering algorithm in this paper. In the proposed approach, to accommodate non-Gaussian distributed cases, the dataset to be clustered in the original space is firstly implicitly mapped into high-dimensional feature space through the kernel function. By introducing the kernel function-based similarity terms in the update formula of the cluster centers, the effect of the objects not belonging to the current cluster on the update of its corresponding center can be counteracted, and simultaneously the influence of regularization coefficient on the clustering result is restricted as well, which can effectively overcome the convergence of the different clusters encountered by traditional ME. In addition, in order to prevent cluster centers from biases caused by the different distribution of the objects in the feature space, the relative density-based weights are also incorporated into the cost function, which can help the proposed approach produce more reasonable and accurate clustering results. In the experiments, the influence of the different parameters on the clustering performance is discussed in detail and some suggestions are also provided. Theoretical analysis and experimental results on several synthetic datasets, UCI benchmark datasets and generated large MNIST handwritten digits datasets demonstrate that the proposed approach is superior to other existing clustering techniques with good robustness. |
| Author | Tao, Xinmin Wang, Ruotong Li, Chenxi Chang, Rui |
| Author_xml | – sequence: 1 givenname: Xinmin surname: Tao fullname: Tao, Xinmin email: taoxinmin@nefu.edu.cn – sequence: 2 givenname: Ruotong surname: Wang fullname: Wang, Ruotong – sequence: 3 givenname: Rui surname: Chang fullname: Chang, Rui – sequence: 4 givenname: Chenxi surname: Li fullname: Li, Chenxi |
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| Cites_doi | 10.1016/j.engfailanal.2016.04.001 10.1007/s00521-016-2208-9 10.1142/S0219635216500151 10.3390/e19090452 10.1016/j.knosys.2018.03.018 10.1016/j.neunet.2017.06.004 10.1016/S0165-0114(97)00126-7 10.1016/j.neucom.2015.09.127 10.1109/TII.2016.2628747 10.1016/j.neucom.2017.06.025 10.1016/j.patcog.2009.09.010 10.1016/j.patrec.2004.03.008 10.1002/cem.2728 10.1109/TNN.2009.2030190 10.1016/j.knosys.2016.12.015 10.1016/j.neucom.2017.01.017 10.1016/j.knosys.2017.05.018 10.3724/SP.J.1001.2008.01683 10.1016/j.patcog.2017.05.017 10.1109/34.85677 |
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| Keywords | Relative density-based weight Maximum entropy clustering algorithm Robustness Clustering |
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| References | Yang, Nataliani (b2) 2017; 71 Zhang, Liu, Zhang (b14) 2017; 93 Zhou, Chen, Chen (b24) 2015; 198 Karayiannis (b26) 2002; vol. 1 Li, Zhang, Lu (b1) 2017; 237 Chrol-Cannon, Jin, Gruning (b5) 2017; 267 Qian, Zhao, Jiang (b10) 2017; 130 Huang, Li, Luo (b7) 2017; 119 Zhou, Liao, Shi (b27) 2014; 29 Wang, Wang, Wang (b21) 2004; 25 Sing, Adhikari, Basu (b25) 2015; 29 Borgelt (b22) 2008; 1 Benavent, Ruiz, Sáez (b28) 2009; 20 Zhou, Fu, Yang (b18) 2014; 57 Thilaga, Vijayalakshmi, Nadarajan (b6) 2016; 15 Akogul, Erisoglu (b17) 2017; 19 Kang, Ji, Ji (b20) 2010; 31 Luo, Peng, Li (b15) 2016; 28 Vidal, Monge, Villalba (b4) 2018 Song, Ji, Sun (b11) 2014; 40 Fred, Jain (b19) 2005; 27 Wu, Wilamowski (b16) 2016; 13 Chemweno, Morag, Sheikhalishahi (b8) 2016; 66 Tan, Chen (b30) 2000 Lei, Xie, Lin (b12) 2008 Bai, Ji, He (b3) 2013; 43 Cheng, Wang (b31) 2016; 31 Xie, Beni (b32) 1991; 13 Cai, Liu, Cao (b9) 2017; 45 Talu (b13) 2017; 2017 Li, Mukaidono (b29) 1999; 102 Z.Deng K.S. Choi, Chung, Wang (b23) 2010; 43 Lei (10.1016/j.knosys.2018.12.007_b12) 2008 Benavent (10.1016/j.knosys.2018.12.007_b28) 2009; 20 Fred (10.1016/j.knosys.2018.12.007_b19) 2005; 27 Talu (10.1016/j.knosys.2018.12.007_b13) 2017; 2017 Tan (10.1016/j.knosys.2018.12.007_b30) 2000 Cai (10.1016/j.knosys.2018.12.007_b9) 2017; 45 Wu (10.1016/j.knosys.2018.12.007_b16) 2016; 13 Chrol-Cannon (10.1016/j.knosys.2018.12.007_b5) 2017; 267 Chemweno (10.1016/j.knosys.2018.12.007_b8) 2016; 66 Wang (10.1016/j.knosys.2018.12.007_b21) 2004; 25 Zhang (10.1016/j.knosys.2018.12.007_b14) 2017; 93 Zhou (10.1016/j.knosys.2018.12.007_b18) 2014; 57 Li (10.1016/j.knosys.2018.12.007_b1) 2017; 237 Luo (10.1016/j.knosys.2018.12.007_b15) 2016; 28 Thilaga (10.1016/j.knosys.2018.12.007_b6) 2016; 15 Sing (10.1016/j.knosys.2018.12.007_b25) 2015; 29 Yang (10.1016/j.knosys.2018.12.007_b2) 2017; 71 Kang (10.1016/j.knosys.2018.12.007_b20) 2010; 31 Huang (10.1016/j.knosys.2018.12.007_b7) 2017; 119 Qian (10.1016/j.knosys.2018.12.007_b10) 2017; 130 Akogul (10.1016/j.knosys.2018.12.007_b17) 2017; 19 Vidal (10.1016/j.knosys.2018.12.007_b4) 2018 Zhou (10.1016/j.knosys.2018.12.007_b27) 2014; 29 Karayiannis (10.1016/j.knosys.2018.12.007_b26) 2002; vol. 1 Z.Deng K.S. Choi (10.1016/j.knosys.2018.12.007_b23) 2010; 43 Zhou (10.1016/j.knosys.2018.12.007_b24) 2015; 198 Borgelt (10.1016/j.knosys.2018.12.007_b22) 2008; 1 Bai (10.1016/j.knosys.2018.12.007_b3) 2013; 43 Li (10.1016/j.knosys.2018.12.007_b29) 1999; 102 Cheng (10.1016/j.knosys.2018.12.007_b31) 2016; 31 Xie (10.1016/j.knosys.2018.12.007_b32) 1991; 13 Song (10.1016/j.knosys.2018.12.007_b11) 2014; 40 |
| References_xml | – volume: 40 start-page: 1754 year: 2014 end-page: 1763 ident: b11 article-title: Brain MR image segmentation algorithm based on Markov random field withimage patch publication-title: Acta Automat. Sinica – volume: 102 start-page: 253 year: 1999 end-page: 258 ident: b29 article-title: Gaussian clustering method based on maximum-fuzzy-entropy interpretation publication-title: Fuzzy Sets & Systems – volume: 27 start-page: 835 year: 2005 end-page: 850 ident: b19 article-title: Combining multiple clusterings using evidence accumulation publication-title: IEEE Comput. Soc. – volume: 31 start-page: 1657 year: 2010 end-page: 1663 ident: b20 article-title: Kernelized fuzzy C-means clustering algorithm and its application publication-title: Chin. J. Sci. Instrum. – start-page: 1683 year: 2008 end-page: 1692 ident: b12 article-title: An efficient clustering algorithm based on optimality of k-means publication-title: J. Softw. – volume: vol. 1 start-page: 630 year: 2002 end-page: 635 ident: b26 article-title: MECA: Maximum entropy clustering algorithm publication-title: Fuzzy Systems, 1994 IEEE World Congress on Computational Intelligence. Proceedings of the Third IEEE Conference on – volume: 31 start-page: 551 year: 2016 end-page: 554 ident: b31 article-title: Support vector data description based on fast clustering analysis publication-title: Control Decis. – volume: 29 start-page: 1991 year: 2014 end-page: 1996 ident: b27 article-title: SVM parameters selection method based on fisher criterion and maximum entropy principle publication-title: Control Decis. – volume: 66 start-page: 19 year: 2016 end-page: 34 ident: b8 article-title: Development of a novel methodology for root cause analysis and selection of maintenance strategy for a thermal power plant: a data exploration approach publication-title: Eng. Fail. Anal. – volume: 25 start-page: 1123 year: 2004 end-page: 1132 ident: b21 article-title: Improving fuzzy C-means clustering based on feature-weight learning publication-title: Pattern Recognit. Lett. – volume: 13 start-page: 1620 year: 2016 end-page: 1628 ident: b16 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. – volume: 20 start-page: 1756 year: 2009 end-page: 1771 ident: b28 article-title: Learning Gaussian mixture models with entropy-based criteria publication-title: IEEE Trans. Neural Netw. – volume: 71 start-page: 45 year: 2017 end-page: 59 ident: b2 article-title: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters publication-title: Pattern Recognit. – volume: 19 start-page: 452 year: 2017 ident: b17 article-title: An approach for determining the number of clusters in a model-based cluster analysis publication-title: Entropy – volume: 198 start-page: 125 year: 2015 end-page: 134 ident: b24 article-title: Fuzzy clustering with the entropy of attribute weights publication-title: Neurocomputing – volume: 130 start-page: 33 year: 2017 end-page: 50 ident: b10 article-title: Knowledge-leveraged transfer fuzzy C-means for texture image segmentation with self-adaptive cluster prototype matching publication-title: Knowl.-Based Syst. – volume: 93 start-page: 240 year: 2017 end-page: 255 ident: b14 article-title: Novel density-based and hierarchical density-based clustering algorithms for uncertain data publication-title: Neural Netw. – volume: 43 start-page: 130 year: 2013 end-page: 134 ident: b3 article-title: New clustering method of mixed-attribute data publication-title: J. Jilin Univ.(Eng. Technol. Ed.) – volume: 15 start-page: 223 year: 2016 end-page: 245 ident: b6 article-title: A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks publication-title: J. Integr. Neurosci. – volume: 45 start-page: 1911 year: 2017 end-page: 1918 ident: b9 article-title: A watershed image segmentation algorithm based on self-adaptive marking and interregional affinity propagation clustering publication-title: ACTA Electron. Sin. – volume: 267 start-page: 644 year: 2017 end-page: 650 ident: b5 article-title: An efficient method for online detection of polychronous patterns in spiking neural networks publication-title: Neurocomputing – volume: 57 start-page: 1 year: 2014 end-page: 8 ident: b18 article-title: Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation publication-title: Sci. China Inf. Sci. – volume: 43 start-page: 767 year: 2010 end-page: 781 ident: b23 article-title: Enhanced soft subspace clustering integrating within-cluster and between-cluster information publication-title: Pattern Recognit. – volume: 237 start-page: 316 year: 2017 end-page: 331 ident: b1 article-title: Interval kernel Fuzzy C-means clustering of incomplete data publication-title: Neurocomputing – volume: 28 start-page: 2545 year: 2016 end-page: 2556 ident: b15 article-title: MWPCA-ICURD: Density-based clustering method discovering specific shape original features publication-title: Neural Comput. Appl. – start-page: 269 year: 2000 end-page: 272 ident: b30 article-title: A Gaussian clustering algorithm based on maximum fuzzy entropy publication-title: J. Univ. Electron. Sci. Tech. China – volume: 119 start-page: 273 year: 2017 end-page: 283 ident: b7 article-title: Matrix-based dynamic updating rough fuzzy approximations for data mining publication-title: Knowl.-Based Syst. – volume: 13 start-page: 841 year: 1991 end-page: 847 ident: b32 article-title: A validity measure for fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 1 start-page: 838 year: 2008 end-page: 844 ident: b22 article-title: Feature weighting and feature selection in fuzzy clustering publication-title: Proc. IEEE Conf. Fuzzy Syt. – volume: 29 start-page: 492 year: 2015 end-page: 505 ident: b25 article-title: A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise publication-title: J. Chemom. – year: 2018 ident: b4 article-title: A novel pattern recognition system for detecting android malware by analyzing suspicious boot sequences publication-title: Knowl.-Based Syst. – volume: 2017 start-page: 1742 year: 2017 end-page: 5468 ident: b13 article-title: Multi-level spectral graph partitioning method publication-title: J. Stat. Mech. Theory Exp. – volume: 66 start-page: 19 year: 2016 ident: 10.1016/j.knosys.2018.12.007_b8 article-title: Development of a novel methodology for root cause analysis and selection of maintenance strategy for a thermal power plant: a data exploration approach publication-title: Eng. Fail. Anal. doi: 10.1016/j.engfailanal.2016.04.001 – volume: 2017 start-page: 1742 issue: 9 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b13 article-title: Multi-level spectral graph partitioning method publication-title: J. Stat. Mech. Theory Exp. – volume: 28 start-page: 2545 issue: 9 year: 2016 ident: 10.1016/j.knosys.2018.12.007_b15 article-title: MWPCA-ICURD: Density-based clustering method discovering specific shape original features publication-title: Neural Comput. Appl. doi: 10.1007/s00521-016-2208-9 – volume: 15 start-page: 223 issue: 02 year: 2016 ident: 10.1016/j.knosys.2018.12.007_b6 article-title: A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks publication-title: J. Integr. Neurosci. doi: 10.1142/S0219635216500151 – volume: 19 start-page: 452 issue: 9 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b17 article-title: An approach for determining the number of clusters in a model-based cluster analysis publication-title: Entropy doi: 10.3390/e19090452 – year: 2018 ident: 10.1016/j.knosys.2018.12.007_b4 article-title: A novel pattern recognition system for detecting android malware by analyzing suspicious boot sequences publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2018.03.018 – volume: 93 start-page: 240 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b14 article-title: Novel density-based and hierarchical density-based clustering algorithms for uncertain data publication-title: Neural Netw. doi: 10.1016/j.neunet.2017.06.004 – volume: 1 start-page: 838 year: 2008 ident: 10.1016/j.knosys.2018.12.007_b22 article-title: Feature weighting and feature selection in fuzzy clustering publication-title: Proc. IEEE Conf. Fuzzy Syt. – volume: 102 start-page: 253 issue: 2 year: 1999 ident: 10.1016/j.knosys.2018.12.007_b29 article-title: Gaussian clustering method based on maximum-fuzzy-entropy interpretation publication-title: Fuzzy Sets & Systems doi: 10.1016/S0165-0114(97)00126-7 – volume: 198 start-page: 125 year: 2015 ident: 10.1016/j.knosys.2018.12.007_b24 article-title: Fuzzy clustering with the entropy of attribute weights publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.09.127 – volume: 13 start-page: 1620 issue: 4 year: 2016 ident: 10.1016/j.knosys.2018.12.007_b16 article-title: A fast density and grid based clustering method for data with arbitrary shapes and noise publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2016.2628747 – start-page: 269 issue: 3 year: 2000 ident: 10.1016/j.knosys.2018.12.007_b30 article-title: A Gaussian clustering algorithm based on maximum fuzzy entropy publication-title: J. Univ. Electron. Sci. Tech. China – volume: 27 start-page: 835 issue: 6 year: 2005 ident: 10.1016/j.knosys.2018.12.007_b19 article-title: Combining multiple clusterings using evidence accumulation publication-title: IEEE Comput. Soc. – volume: 267 start-page: 644 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b5 article-title: An efficient method for online detection of polychronous patterns in spiking neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.06.025 – volume: 43 start-page: 767 year: 2010 ident: 10.1016/j.knosys.2018.12.007_b23 article-title: Enhanced soft subspace clustering integrating within-cluster and between-cluster information publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2009.09.010 – volume: 25 start-page: 1123 year: 2004 ident: 10.1016/j.knosys.2018.12.007_b21 article-title: Improving fuzzy C-means clustering based on feature-weight learning publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2004.03.008 – volume: 29 start-page: 492 issue: 9 year: 2015 ident: 10.1016/j.knosys.2018.12.007_b25 article-title: A modified fuzzy C-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise publication-title: J. Chemom. doi: 10.1002/cem.2728 – volume: 43 start-page: 130 issue: 1 year: 2013 ident: 10.1016/j.knosys.2018.12.007_b3 article-title: New clustering method of mixed-attribute data publication-title: J. Jilin Univ.(Eng. Technol. Ed.) – volume: 20 start-page: 1756 issue: 11 year: 2009 ident: 10.1016/j.knosys.2018.12.007_b28 article-title: Learning Gaussian mixture models with entropy-based criteria publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2009.2030190 – volume: 29 start-page: 1991 issue: 11 year: 2014 ident: 10.1016/j.knosys.2018.12.007_b27 article-title: SVM parameters selection method based on fisher criterion and maximum entropy principle publication-title: Control Decis. – volume: 119 start-page: 273 issue: C year: 2017 ident: 10.1016/j.knosys.2018.12.007_b7 article-title: Matrix-based dynamic updating rough fuzzy approximations for data mining publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2016.12.015 – volume: 237 start-page: 316 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b1 article-title: Interval kernel Fuzzy C-means clustering of incomplete data publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.017 – volume: 45 start-page: 1911 issue: 8 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b9 article-title: A watershed image segmentation algorithm based on self-adaptive marking and interregional affinity propagation clustering publication-title: ACTA Electron. Sin. – volume: 130 start-page: 33 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b10 article-title: Knowledge-leveraged transfer fuzzy C-means for texture image segmentation with self-adaptive cluster prototype matching publication-title: Knowl.-Based Syst. doi: 10.1016/j.knosys.2017.05.018 – start-page: 1683 issue: 7 year: 2008 ident: 10.1016/j.knosys.2018.12.007_b12 article-title: An efficient clustering algorithm based on optimality of k-means publication-title: J. Softw. doi: 10.3724/SP.J.1001.2008.01683 – volume: 71 start-page: 45 year: 2017 ident: 10.1016/j.knosys.2018.12.007_b2 article-title: Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters publication-title: Pattern Recognit. doi: 10.1016/j.patcog.2017.05.017 – volume: 31 start-page: 551 issue: 3 year: 2016 ident: 10.1016/j.knosys.2018.12.007_b31 article-title: Support vector data description based on fast clustering analysis publication-title: Control Decis. – volume: vol. 1 start-page: 630 year: 2002 ident: 10.1016/j.knosys.2018.12.007_b26 article-title: MECA: Maximum entropy clustering algorithm – volume: 40 start-page: 1754 issue: 8 year: 2014 ident: 10.1016/j.knosys.2018.12.007_b11 article-title: Brain MR image segmentation algorithm based on Markov random field withimage patch publication-title: Acta Automat. Sinica – volume: 31 start-page: 1657 issue: 7 year: 2010 ident: 10.1016/j.knosys.2018.12.007_b20 article-title: Kernelized fuzzy C-means clustering algorithm and its application publication-title: Chin. J. Sci. Instrum. – volume: 57 start-page: 1 issue: 11 year: 2014 ident: 10.1016/j.knosys.2018.12.007_b18 article-title: Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation publication-title: Sci. China Inf. Sci. – volume: 13 start-page: 841 issue: 13 year: 1991 ident: 10.1016/j.knosys.2018.12.007_b32 article-title: A validity measure for fuzzy clustering publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.85677 |
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| SubjectTerms | Algorithms Bias Clustering Convergence Datasets Density Digits Entropy Experiments Function Fuzzy sets Handwriting Implementation Iterative methods Kernel functions Maximum entropy Maximum entropy clustering algorithm Normal distribution Object recognition Regularization Relative density-based weight Robustness Simplicity |
| Title | Density-sensitive fuzzy kernel maximum entropy clustering algorithm |
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