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Adaptive fuzzy clustering by fast search and find of density peaks

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Abstract

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.

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References

  1. Li K et al (2013) Personalized multi-modality image management and search for mobile devices. Pers Ubiquitous Comput 17(8):1817–1834

    Article  Google Scholar 

  2. Jiwen L, Erin LV, Xiuzhuang Z, Jie Z (2015) Learning compact binary face descriptor for face recognition. IEEE Trans Pattern Anal Mach Intell (TPAMI) 37(10):2041–2256

    Article  Google Scholar 

  3. Lu J, Zhou X, Tan Y-P, Shang Y, Zhou J (2014) Neighborhood repulsed metric learning for kinshipverification. IEEE Trans Pattern Anal Mach Intell (T-PAMI) 36(2):331–345

    Article  Google Scholar 

  4. Lu J, Tan Y-P, Wang G (2013) Discriminative multimanifold analysis for face ecognition from a single training sample per person. IEEE Trans Pattern Anal Mach Intell (T-PAMI) 35(1):39–51

    Article  Google Scholar 

  5. Lu J, Liong VE, Zhou J (2015) Cost-sensitive local binary feature learning for facial ageestimation. IEEE Trans Image Process (T-IP) 24(12):5356–5368

    Article  MathSciNet  Google Scholar 

  6. Yan Y, Qian Y, Sharif H, Tipper D (2012) A survey on cyber security for smart grid communications. IEEE Commun Surv Tutor 14(4):998–1010

    Article  Google Scholar 

  7. Portnoy L, Eskin E, Stolfo S (2001) Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA-2001) pp 5–8

  8. Ahn C-S, Sang-Yeob O (2014) Robust vocabulary recognition clustering model using an average estimator least mean square filter in noisy environments. Pers Ubiquitous Comput 18(6):1295–1301

    Article  Google Scholar 

  9. Guo L, Ai C, Wang X, Cai Z, Li Y (2009) Real Time Clustering of Sensory Data in Wireless Sensor Networks. The 28th IEEE International Performance Computing and Communications Conference (IPCCC)

  10. Yeganova L, Kim W, Kim S, Wilbur WJ (2014) Retro: concept-based clustering of biomedical topical sets. Bioinformatics 30(22):3240–3248

    Article  Google Scholar 

  11. Xu C, Zhengchang S (2015) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 37(10):2041–2256

    Google Scholar 

  12. Shuji S, Kakuta M, Ishida T, Akiyama Y (2015) Faster sequence homology searches by clustering subsequences. Bioinformatics 31(8):1183–1190

    Article  Google Scholar 

  13. Shi Y, Hasan M, Cai Z, Lin G, Schuurmans D (2012) Linear coherent bi-clustering via beam searching and sample set clustering. Discrete Math Algorithms Appl 4(2):1250023

    Article  MathSciNet  MATH  Google Scholar 

  14. Cai Z, Heydari M, Lin G (2005) Clustering binary oligonucleotide fingerprint vectors for DNA clone classification analysis. J Comb Optim 9(2):199–211

    Article  MathSciNet  MATH  Google Scholar 

  15. Nicovich Philip R et al (2015) Analysis of nanoscale protein clustering with quantitative localization microscopy. Biophys J 108(2):475a

    Article  Google Scholar 

  16. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22(13):1658–1659

    Article  Google Scholar 

  17. Shaw MKE (2015) K-means clustering with automatic determination of K using a Multiobjective Genetic Algorithm with applications to microarray gene expression data. Dissertation, San Diego State University

  18. Chang M-S, Chen L-H, Hung L-J, Rossmanith P, Guan-Han W (2014) Exact algorithms for problems related to the densest k-set problem. Inf Process Lett 114(9):510–513

    Article  MathSciNet  MATH  Google Scholar 

  19. Kannuri L, Murty MR, Satapathy SC (2015) Partition based clustering using genetic algorithm and teaching learning based optimization: performance analysis. Adv Intell Syst Comput 338:191–200

    Article  Google Scholar 

  20. MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: proceedings of the fifth Berkeley symposium on mathematical statistics and probability, vol 1, no 14, pp 281–297

  21. Park H-S, Jun C-H (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl 36(2):3336–3341

    Article  Google Scholar 

  22. Lovely Sharma P, Ramya KA (2013) Review on density based clustering algorithms for very large datasets. Int J Emerg Technol Adv Eng 3(12):398–403

    Google Scholar 

  23. Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96(34):226–231

    Google Scholar 

  24. Parimala M, Lopez D, Senthilkumar NC (2011) A survey on density based clustering algorithms for mining large spatial databases. Int J Adv Sci Technol 31(1):216–223

    Google Scholar 

  25. Shah Glory H, Bhensdadia CK, Ganatra Amit P (2012) An empirical evaluation of density-based clustering techniques. Int J Soft Comput Eng (IJSCE) 2(1):2231–2307

    Google Scholar 

  26. Liu P, Zhou D, Wu N (2007) VDBSCAN: varied density based spatial clustering of applications with noise. In: Proceedings: Service Systems and Service Management 2007, pp 1–4

  27. Mehmood R, Zhang G, Bie R, Dawood H, Ahmad H (2016) Clustering by fast search and find of density peaks via heat diffusion. Neurocomputing. doi:10.1016/j.neucom.2016.01.102i

    Google Scholar 

  28. Birant D, Kut A (2007) ST-DBSCAN: an algorithm for clustering spatial-temporal data. Data Knowl Eng 60(1):208–221

    Article  Google Scholar 

  29. Chen T, Zhang NL, Liu T, Poon KM, Wang Y (2012) Model-based multidimensional clustering of categorical data. Artif Intell 176(1):2246–2269

    Article  MathSciNet  MATH  Google Scholar 

  30. Mann AK, Kaur N (2013) Survey paper on clustering techniques. Int J Sci Eng Technol Res (IJSETR) 2(4):803–806

    Google Scholar 

  31. Murtagh F, Contreras P (2012) Algorithms for hierarchical clustering: an overview. Wiley Interdiscip Rev: Data Min Knowl Discov 2(1):86–97

    MathSciNet  Google Scholar 

  32. Chen N, Ze-shui X, Xia M (2014) Hierarchical hesitant fuzzy K-means clustering algorithm. Appl Math A J Chin Univ 29(1):1–17

    Article  MathSciNet  MATH  Google Scholar 

  33. Jaeger D, Barth J, Niehues A, Fufezan C (2014) pyGCluster, a novel hierarchical clustering approach. Bioinformatics 30(6):896–898

    Article  Google Scholar 

  34. Jacques J, Preda C (2014) Functional data clustering: a survey. Adv Data Anal Classif 8(3):231–255

    Article  MathSciNet  Google Scholar 

  35. Parikh M, Varma T (2014) Survey on different grid based clustering algorithms. Int J Adv Res Comput Sci Manag Stud 2(2):427–430

    Google Scholar 

  36. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976

    Article  MathSciNet  MATH  Google Scholar 

  37. Cheng Y (1995) Mean shift, mode seeking, and clustering. IEEE Trans Pattern Anal Mach Intell 17(8):790–799

    Article  Google Scholar 

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

    Article  Google Scholar 

  39. Fukunaga K, Hostetler L (1975) The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans Inf Theory 21:32–40

    Article  MathSciNet  MATH  Google Scholar 

  40. Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data (TKDD) 1(1):1–30

    Article  Google Scholar 

  41. Fu L, Medico E (2007) FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinform vol 8, artical no. 3

  42. Chang H, Yeung DY (2008) Robust path-based spectral clustering. Pattern Recognit 41(2):191–203

    Article  MATH  Google Scholar 

  43. 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 

  44. Franti P, Virmajoki O, Hautamaki V (2006) Fast agglomerative clustering using a k-nearest neighbor graph. IEEE Trans Pattern Anal Mach Intell 28(11):1875–1881

    Article  Google Scholar 

  45. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  46. Karkkainen I, Franti P (2002) Dynamic local search for clustering with unknown number of clusters. In: Proceedings of International Conference on Pattern Recognition, vol 16, no 2, pp 240–243

  47. Franti P, Virmajoki O (2006) Iterative shrinking method for clustering problems. Pattern Recognit 39(5):761–775

    Article  MATH  Google Scholar 

Download references

Acknowledgments

This research is sponsored by National Natural Science Foundation of China (Nos. 61171014,61371185, 61401029, 61472044, 61472403, 61571049) and the Fundamental Research Funds for the Central Universities (Nos. 2014KJJCB32, 2013NT57) and by SRF for ROCS, SEM.

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Authors and Affiliations

  1. College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China

    Rongfang Bie, Rashid Mehmood & Shanshan Ruan

  2. Department of Computer Science and Information Technology, University of Management Sciences and Information Technology, Kotli, AJK, Pakistan

    Rashid Mehmood

  3. Business School, Beijing Normal University, Beijing, 100875, China

    Yunchuan Sun

  4. Department of Computer Engineering, University of Engineering and Technology, Taxila, Pakistan

    Hussain Dawood

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  1. Rongfang Bie
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  2. Rashid Mehmood
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  3. Shanshan Ruan
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  4. Yunchuan Sun
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  5. Hussain Dawood
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Correspondence to Yunchuan Sun.

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Bie, R., Mehmood, R., Ruan, S. et al. Adaptive fuzzy clustering by fast search and find of density peaks. Pers Ubiquit Comput 20, 785–793 (2016). https://doi.org/10.1007/s00779-016-0954-4

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