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DPCG: an efficient density peaks clustering algorithm based on grid

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Abstract

To deal with the complex structure of the data set, density peaks clustering algorithm (DPC) was proposed in 2014. The density and the delta-distance are utilized to find the clustering centers in the DPC method. It detects outliers efficiently and finds clusters of arbitrary shape. But unfortunately, we need to calculate the distance between all data points in the first process, which limits the running speed of DPC algorithm on large datasets. To address this issue, this paper introduces a novel approach based on grid, called density peaks clustering algorithm based on grid (DPCG). This approach can overcome the operation efficiency problem. When calculating the local density, the idea of the grid is introduced to reduce the computation time based on the DPC algorithm. Neither it requires calculating all the distances nor much input parameters. Moreover, DPCG algorithm successfully inherits the all merits of the DPC algorithm. Experimental results on UCI data sets and artificial data show that the DPCG algorithm is flexible and effective.

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References

  1. Morris K, Mcnicholas PD (2016) Clustering, classification, discriminant analysis, and dimension reduction via generalized hyperbolic mixtures. Comput Stat Data Anal 97:133–150

    Article  MathSciNet  Google Scholar 

  2. Cai R, Zhang Z, Tung AKH et al (2014) A general framework of hierarchical clustering and its applications. Inf Sci 272(C):29–48

    Article  MathSciNet  Google Scholar 

  3. Yu Z, Luo P, You J et al (2015) Incremental semi-supervised clustering ensemble for high dimensional data clustering. IEEE Trans Knowl Data Eng 28(3):701–714

    Article  Google Scholar 

  4. Xia Z, Wang X, Zhang L et al (2016) A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing. IEEE Transactions on Information Forensics and Security 1–1

  5. Yu Z, Li L, Liu J et al (2015) Adaptive noise immune cluster ensemble using affinity propagation. IEEE Trans Knowl Data Eng 27(12):3176–3189

    Article  Google Scholar 

  6. Yu Z, Chen H, You J et al (2015) Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data. IEEE/ACM Trans Comput Biol Bioinf 12(4):887–901

    Article  Google Scholar 

  7. Ren Y, Shen J, Wang J et al (2015) Mutual verifiable provable data auditing in public cloud storage. J Internet Technol 16(2):317–323

    Google Scholar 

  8. Bayat A (2013) Uniform voltage distribution based constructive algorithm for optimal reconfiguration of electric distribution networks. Electr Power Syst Res 104(9):146–155

    Article  Google Scholar 

  9. Yu Z, Zhu X, Wong HS et al (2016) Distribution-Based Cluster Structure Selection. IEEE Transactions on Cybernetics

  10. Zhang Z, Ding F, Liu X (2011) Hierarchical gradient based iterative parameter estimation algorithm for multivariable output error moving average systems. Comput Math Appl 61(3):672–682

    Article  MathSciNet  MATH  Google Scholar 

  11. Fraley C, Raftery AE (2011) Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc 97(458):611–631

    Article  MathSciNet  MATH  Google Scholar 

  12. He Y, Tan H, Luo W, et al (2011) MR-DBSCAN: an efficient parallel density-based clustering algorithm using mapreduce. International Conference on Parallel and Distributed Systems Proceedings, pp 473–480

  13. Li C, Li L, Zhang J et al (2012) Highly efficient and exact method for parallelization of grid-based algorithms and its implementation in DelPhi. J Comput Chem 33(24):1960–1966

    Article  Google Scholar 

  14. Ros F, Guillaume S (2016) DENDIS: a new density-based sampling for clustering algorithm. Expert Syst Appl 56:349–359

    Article  Google Scholar 

  15. Nanda SJ, Panda G (2015) Design of computationally efficient density-based clustering algorithms. Data Knowl Eng 95:23–38

    Article  Google Scholar 

  16. Yue SH, Wang JS, Tao G et al (2010) An unsupervised grid-based approach for clustering analysis. Sciece China Inf Sci 53(7):1345–1357

    Article  Google Scholar 

  17. Huang J, Zhang X (2013) An incremental grid clustering algorithm based on density-dimension-tree. International Conference on Machine Learning and Cybernetics. IEEE, pp 356–361

  18. Zhang D, Tian H, Ying P et al (2012) A clustering algorithm based on density-grid for stream data. International Conference on Parallel and Distributed Computing, Applications, and Technologies (PDCAT), 398–403

  19. Zhong Z (2011) A kind of data stream clustering algorithm based on grid-density. Commun Comput Inf Sci 215:418–423

    Google Scholar 

  20. Rodrigitez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

    Article  Google Scholar 

  21. Zheng Y, Byeungwoo J, Xu D et al (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):4024–4028

    Google Scholar 

  22. Arora P, Deepali, Varshney S (2016) Analysis of K-Means and K-Medoids algorithm for big data. Procedia Comput Sci 78:507–512

    Article  Google Scholar 

  23. Jia H, Ding S, Du M (2015) Self-tuning P-spectral clustering based on shared nearest neighbors. Cogn Comput 7(5):622–632

    Article  Google Scholar 

  24. Yu Z, Li L, You J et al (2012) SC3: triple spectral clustering-based consensus clustering framework for class discovery from cancer gene expression profiles. IEEE/ACM Trans Comput Biol Bioinf 9(6):1751–1765

    Article  Google Scholar 

  25. Cheng Q, Liu Z, Huang J et al (2016) Community detection in hypernetwork via density-ordered tree partition. Appl Math Comput 276(C):384–393

  26. Yin Y, Wang X, Xu D et al (2016) Robust visual detection-learning-tracking framework for autonomous aerial refueling of UAVs. IEEE Trans Instrum Meas 65(3):1–12

    Article  Google Scholar 

  27. Du M, Ding S, Jia H (2016) Study on density peaks clustering based on k-nearest neighbors and principal component analysis. Knowl-Based Syst 99:135–145

    Article  Google Scholar 

  28. Wang S, Wang D, Caoyuan LI et al (2016) Clustering by fast search and find of density peaks with data field. Chin J Electron 25(3):397–402

    Article  Google Scholar 

  29. Guo L, Lin JH, Guo Q et al (2015) Identifying multiple influential spreaders in term of the distance-based coloring. Phys Lett A 380(7–8):837–842

    Google Scholar 

  30. Chen B, Shu H, Coatrieux G et al (2014) Color image analysis by quaternion-type moments. J Math Imaging Vision 51(1):124–144

    Article  MathSciNet  MATH  Google Scholar 

  31. Kumar KM, Reddy ARM (2016) A fast DBSCAN clustering algorithm by accelerating neighbor searching using Groups method. Pattern Recogn 58:39–48

    Article  Google Scholar 

  32. Kanungo T, Mount DM, Netanyahu NS et al (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892

    Article  Google Scholar 

  33. Xie J, Gao H, Xie W et al (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K -nearest neighbors. Inf Sci 354:19–40

    Article  Google Scholar 

  34. Wang S, Wang D, Caoyuan LI et al (2016) Clustering by fast search and find of density peaks with data field. Chin J Electron 25(3):397–402

    Article  Google Scholar 

  35. Liang Z, Chen P (2016) Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering. Pattern Recogn Lett 73(C):52–59

  36. Ban Z, Liu J, Yuan L et al (2015) A modified density-based clustering algorithm and its implementation. PATTERN RECOGNITION AND COMPUTER VISION, 9813

  37. Wang D, Zhang B, Wang K (2014) A vertex-clustering algorithm based on the cluster-clique. Algorithms Archit Parallel Process 8631:376–385

    Google Scholar 

  38. Bohn B, Garcke J, Griebel M (2016) A sparse grid based method for generative dimensionality reduction of high-dimensional data. J Comput Phys 309:1–17

    Article  MathSciNet  MATH  Google Scholar 

  39. Fu Z, Sun X, Liu Q, et al (2015) Achieving efficient cloud search services: multi-keyword ranked search over encrypted cloud data supporting parallel computing. Ieice Trans Commun E98.B(1):190–200

  40. Xia Z, Wang X, Sun X et al (2016) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Trans Parallel Distrib Syst 27(2):340–352

    Article  Google Scholar 

  41. Veenman CJ, Reinders MJT, Backer E (2002) A maximum variance cluster algorithm. IEEE Trans Pattern Anal Mach Intell 24(9):1273–1280

    Article  Google Scholar 

  42. Fränti P, Virmajoki O (2006) Iterative shrinking method for clustering problems. Pattern Recogn 39(5):761–775

    Article  MATH  Google Scholar 

  43. Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data 1(1):341–352

    Article  Google Scholar 

  44. Fu L, Medico E (2007) FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinf 8(1):1–15

    Article  Google Scholar 

  45. Kärkkäinen I, Fränti P (2002) Dynamic local search algorithm for the clustering problem. Technical Report A-2002-6, Department of Computer Science, University of Joensuu

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Xiao Xu, Shifei Ding & Mingjing Du

  2. Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China

    Shifei Ding

  3. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yu Xue

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  1. Xiao Xu
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  2. Shifei Ding
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  3. Mingjing Du
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  4. Yu Xue
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Correspondence to Shifei Ding.

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Xu, X., Ding, S., Du, M. et al. DPCG: an efficient density peaks clustering algorithm based on grid. Int. J. Mach. Learn. & Cyber. 9, 743–754 (2018). https://doi.org/10.1007/s13042-016-0603-2

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  • DOI: https://doi.org/10.1007/s13042-016-0603-2

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