[1]
J. R. Hobbs, Granularity, in Readings in Qualitative Reasoning about Physical Systems, D. S. Weld and J. De Kleer, eds. Elsevier, 1990, pp. 542-545.
[2]
L. Zhang and B. Zhang, Theory and Application of Problem Solving. Beijing, China: Tsinghua University Press, 1990.
[3]
J. S. R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Trans. Syst., Man, and Cybern., vol. 23, no. 3, pp. 665-685, 1993.
[4]
G. Y. Wang and H. B. Shi, TMLNN: Triple-valued or multiple-valued logic neural network, IEEE Trans. Neural Netw., vol. 9, no. 6, pp. 1099-1117, 1998.
[5]
L. Y. Zhu, G. W. Yuan, and Q. Du, An efficient explicit/implicit domain decomposition method for convection-diffusion equations, Numer. Methods Partial Differ. Equ., vol. 26, no. 4, pp. 852-873, 2010.
[6]
R. E. Jenkins and B. P. Yuhas, A simplified neural network solution through problem decomposition: The case of the truck backer-upper, IEEE Trans. Neural Netw., vol. 4, no. 4, pp. 718-720, 1993.
[7]
S. Thiria, C. Mejia, F. Badran, and M. Crépon, Multimodular architecture for remote sensing operations, in Proc. 4th Int. Conf. Neural Information Processing Systems, Denver, CO, USA, 1991, pp. 675-682.
[8]
R. Rifkin and A. Klautau, In defense of one-vs-all classification, J. Mach. Learn. Res., vol. 5, pp. 101-141, 2004.
[9]
R. Perdisci, G. F. Gu, and W. Lee, Using an ensemble of one-class SVM classifiers to harden payload-based anomaly detection systems, in Proc. 6th Int. Conf. Data Mining, Hong Kong, China, 2006, pp. 488-498.
[10]
L. C. Cobo, C. L. Isbell Jr., and A. L. Thomaz, Automatic task decomposition and state abstraction from demonstration, in Proc. 11th Int. Conf. Autonomous Agents and Multiagent Systems, Valencia, Spain, 2012, pp. 483-490.
[11]
J. Xu and G. Y. Wang, Leading tree in DPCLUS and its impact on building hierarchies, arXiv preprint arXiv: 1506.03879, 2015.
[12]
A. Rodriguez and A. Laio, Clustering by fast search and find of density peaks, Science, vol. 344, no. 6191, pp. 1492-1496, 2014.
[13]
K. Sun, X. R. Geng, and L. Y. Ji, Exemplar component analysis: A fast band selection method for hyperspectral imagery, IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 998-1002, 2015.
[14]
Y. W. Chen, D. H. Lai, H. Qi, J. L. Wang, and J. X. Du, A new method to estimate ages of facial image for large database, Multimed. Tools Appl., vol. 75, no. 5, pp. 2877-2895, 2016.
[15]
H. Wu and Y. Wan, Clustering assisted fundamental matrix estimation, arXiv preprint arXiv: 1504.03409, 2015.
[16]
K. M. Dean, L. M. Davis, J. L. Lubbeck, P. Manna, P. Friis, A. E. Palmer, and R. Jimenez, High-speed multiparameter photophysical analyses of fluorophore libraries, Anal. Chem., vol. 87, no. 10, pp. 5026-5030, 2015.
[17]
A. P. Zeng and Y. P. Huang, A text classification algorithm based on Rocchio and hierarchical clustering, in Proc. 7th Int. Conf. Advanced Intelligent Computing, Zhengzhou, China, 2011, pp. 432-439.
[18]
M. M. Wang, W. L. Zuo, and Y. Wang, An improved density peaks-based clustering method for social circle discovery in social networks, Neurocomputing, vol. 179, pp. 219-227, 2016.
[19]
W. He and M. D. Xing, Classification method for POLSAR images based on find of density peak, (in Chinese), Syst. Eng. Electron., vol. 38, no. 1, pp. 60-63, 2016.
[20]
J. Xu, G. Y. Wang, and W. H. Deng, DenPEHC: Density peak based efficient hierarchical clustering, Inf. Sci., vol. 373, pp. 200-218, 2016.
[21]
G. Y. Wang, J. Xu, Q. H. Zhang, and Y. C Liu, Multi-granularity intelligent information processing, in Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, Y. Y. Yao, Q. H. Hu, H. Yu, and J. W. Grzymala-Busse, eds. Springer, 2015.
[22]
S. L. Wang, D. K. Wang, C. Y. Li, Y. Li, and G. Y. Ding, Clustering by fast search and find of density peaks with data field, Chin. J. Electron., vol. 25, no. 3, pp. 397-402, 2016.
[23]
T. Qiu, K. F. Yang, C. Y. Li, and Y. J. Li, A physically inspired clustering algorithm: To evolve like particles, arXiv preprint arXiv: 1412.5902, 2014.
[24]
M. L. Du, S. F. Ding, and H. J. Jia, Study on density peaks clustering based on k-nearest neighbors and principal component analysis, Knowl.-Based Syst., vol. 99, pp. 135-145, 2016.
[25]
X. H. Xu, Y. S. Ju, Y. L. Liang, and P. He, Manifold density peaks clustering algorithm, in Proc. 3rd Int. Conf. Advanced Cloud and Big Data, Yangzhou, China, 2015, pp. 311-318.
[26]
W. K. Zhang and J. Li, Extended fast search clustering algorithm: Widely density clusters, no density peaks, arXiv preprint arXiv: 1505.05610, 2015.
[27]
G. Karypis, E. H. Han, and V. Kumar, Chameleon: Hierarchical clustering using dynamic modeling, Computer, vol. 32, no. 8, pp. 68-75, 1999.
[28]
A. Biswas and B. Biswas, Investigating community structure in perspective of ego network, Expert Syst. Appl., vol. 42, no. 20, pp. 6913-6934, 2015.
[29]
J. Fagnan, O. Zaïane, and D. Barbosa, Using triads to identify local community structure in social networks, in Proc. 2014 IEEE/ACM Int. Conf. Advances in Social Networks Analysis and Mining, Beijing, China, 2014, pp. 108-112.
[30]
K. H. Lim and A. Datta, A seed-centric community detection algorithm based on an expanding ring search, in Proc. 1st Australasian Web Conf., Adelaide, South Australia, 2013.
[31]
Z. Yakoubi and R. Kanawati, LICOD: A leader-driven algorithm for community detection in complex networks, Vietnam J. Comput. Sci., vol. 1, no. 4, pp. 241-256, 2014.
[32]
D. Lusseau, K. Schneider, O. J. Boisseau, P. Haase, E. Slooten, and S. M. Dawson, The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations, Behav. Ecol. Sociobiol., vol. 54, no. 4, pp. 396-405, 2003.
[33]
W. W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res., vol. 33, no. 4, pp. 452-473, 2015.
[34]
M. Girvan and M. E. J. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA, vol. 99, no. 12, pp. 7821-7826, 2002.
[35]
A. Lancichinetti, S. Fortunato, and F. Radicchi, Benchmark graphs for testing community detection algorithms, Phys. Rev. E, vol. 78, no. 4, 2008.
[36]
N. X. Vinh, J. Epps, and J. Bailey, Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance, J. Mach. Learn. Res., vol. 11, pp. 2837-2854, 2010.
[37]
M. A. Tabarzad and A. Hamzeh, A heuristic local community detection method (HLCD), Appl. Intell., vol. 46, no. 1, pp. 62-78, 2017.
[38]
X. W. Xu, N. Yuruk, Z. D. Feng, and T. A. J. Schweiger, SCAN: A structural clustering algorithm for networks, in Proc. 13th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, San Jose, CA, USA, 2007, pp. 824-833.
[39]
D. Shah and T. Zaman, Community detection in networks: The leader-follower algorithm, arXiv preprint arXiv: 1011.0774 , 2010.
