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Mathematical Problems in Engineering
Volume 2018, Article ID 3742048, 16 pages
https://doi.org/10.1155/2018/3742048
Research Article

A -Deviation Density Based Clustering Algorithm

1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
2College of Electronic Information, Zhejiang Wanli University, Ningbo 315100, China

Correspondence should be addressed to Yang Dongyong; nc.ude.tujz@ydgnay

Received 2 October 2017; Revised 29 December 2017; Accepted 17 January 2018; Published 26 February 2018

Academic Editor: Erik Cuevas

Copyright © 2018 Chen Jungan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Ester, H. P. Krigel, J. Sander, and X. Xu, “A Density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the International Conference on Knowledge Discovery and DataMining, pp. 226–231, 1996.
  2. O. Uncu, W. A. Gruver, D. B. Kotak, D. Sabaz, Z. Alibhai, and C. Ng, “GRIDBSCAN: GRId density-based spatial clustering of applications with noise,” in Proceedings of the 2006 IEEE International Conference on Systems, Man and Cybernetics, pp. 2976–2981, Taiwan, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Xiaoyun, M. Yufang, Z. Yan, and W. Ping, “GMDBSCAN: Multi-density DBSCAN cluster based on grid,” in Proceedings of the IEEE International Conference on e-Business Engineering, ICEBE'08, pp. 780–783, China, October 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Chen, W. Liu, H. Qiu, and J. Lai, “APSCAN: A parameter free algorithm for clustering,” Pattern Recognition Letters, vol. 32, no. 7, pp. 973–986, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Peng, Z. Dong, and W. Naijun, “VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise,” in Proceedings of the ICSSSM'07: 2007 International Conference on Service Systems and Service Management, China, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. T.-Q. Huang, Y.-Q. Yu, K. Li, and W.-F. Zeng, “Reckon the parameter of DBSCAN for multi-density data sets with constraints,” in Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, pp. 375–379, China, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. Z. Xiong, R. Chen, Y. Zhang, and X. Zhang, “Multi-density DBSCAN algorithm based on density levels partitioning,” Journal of Information and Computational Science, vol. 9, no. 10, pp. 2739–2749, 2012. View at Google Scholar · View at Scopus
  8. J. Hou, H. Gao, and X. Li, “DSets-{DBSCAN}: a parameter-free clustering algorithm,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3182–3193, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. A. Ram, A. Sharma, A. S. Jalal, R. Singh, and A. Agrawal, “An enhanced density based spatial clustering of applications with noise,” in Proceedings of the 2009 IEEE International Advance Computing Conference, IACC 2009, pp. 1475–1478, India, March 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. B. Borah and D. K. Bhattacharyya, “DDSC: A density differentiated spatial clustering technique,” Journal of Computers, vol. 3, no. 2, pp. 72–79, 2008. View at Google Scholar · View at Scopus
  11. D. Pascual, F. Pla, and J. Sanchez, “Non parametric local density-based clustering for multimodal overlapping distributions,” Intelligent Data Engineering and Automated Learning IDEAL, pp. 671–678, 2006. View at Google Scholar
  12. W. Ashour and S. Sunoallah, “Multi density DBSCAN,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 6936, pp. 446–453, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Debnath, P. K. Tripathi, and R. Elmasri, “K-DBSCAN: Identifying spatial clusters with differing density levels,” in Proceedings of the 2015 International Workshop on Data Mining with Industrial Applications, DMIA 2015, pp. 51–60, Paraguay, September 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. M. E. Houle, H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek, “Can shared-neighbor distances defeat the curse of dimensionality?” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 6187, pp. 482–500, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in Proceedings of the 8th International Conference on Computer Vision, pp. 416–423, July 2001. View at Scopus
  16. A. Laio and A. Rodriguez, “Clustering by fast search and find of density peaks,” Science, vol. 344, no. 6191, pp. 1492–1496, 2014. View at Publisher · View at Google Scholar · View at Scopus
  17. M. P. Sampat, Z. Wang, S. Gupta, A. C. Bovik, and M. K. Markey, “Complex wavelet structural similarity: a new image similarity index,” IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2385–2401, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. Y. LeCun and C. Cortes, “The mnist database of handwritten digits,” Tech. Rep., Available electronically at http://yann.lecun.com/exdb/mnist, 2012.