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Pattern Analysis and Applications

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12.05.2021 | Theoretical advances

A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force

verfasst von: Guan Junyi , Sheng li, He Xiongxiong, Chen Jiajia

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2021

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Abstract

Clustering by fast search and find of density peaks (DPC) is a popular clustering method based on density and distance. In DPC, each non-center point’s cluster label is led by its nearest point with higher density, which may cause some misclassifications of non-center points and interfere with the choice of correct cluster centers in the decision graph. To avoid these defects, we propose a novel clustering algorithm that automatically generates clusters without using the decision graph based on the Normal-neighbor and Merging force (NM-DPC). We conduct a series of experiments on various challenging synthetic datasets. Experimental results demonstrate that NM-DPC can better identify clusters of complex shapes and automatically recognize the number of clusters.

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Jain AK (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef

Hansen P, Jaumard B (1997) Cluster analysis and mathematical programming. Math Program 79(1–3):191–215MathSciNetMATH

Xu R, Wunsch D II (2007) Computational intelligence in clustering algorithms, with applications

Xu R, Wunsch DC (2010) Clustering algorithms in biomedical research: a review. IEEE Rev Biomed Eng 3:120–154CrossRef

Jain AK, Dubes RC (1988) Algorithms for clustering data. Technometrics 32(2):227–229MATH

X Qian, Y Wu, M Li, Y Ren, S Jiang, Z Li (2020) LAST: location-appearance-semantic-temporal clustering based POI summarization. IEEE Trans Multimed

Wu Z, Leahy Richard M (1993) An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans Pattern Anal Mach Intell 15(11):1101–1113CrossRef

Berry Michael W, Castellanos Malu (2007) Survey of text mining: clustering, classification, and retrieval. Springer, Berlin

Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255–260MathSciNetCrossRef

10.

Macqueen J (1965) Some methods for classification and analysis of multivariate observations. In: Proceedings of Berkeley symposium on mathematical statistics and probability

11.

Jain AK (2008) Data clustering: 50 years beyond k-means. In: Machine learning and knowledge discovery in databases

12.

Rahman MA, Islam MZ (2014) A hybrid clustering technique combining a novel genetic algorithm with K-Means. Knowl Based Syst 7:1345–365

13.

Tzortzis G, Likas A (2014) The MinMax K-Means clustering algorithm. Pattern Recognit 47(7):2505–2516CrossRef

14.

Likas A, Vlassis N, Verbeek JJ (2003) The global K-Means clustering algorithm. Pattern Recognit 36(2):451–46CrossRef

15.

Xie J, Jiang S, Xie W, Gao X (2011) An efficient global K-Means clustering algorithm. JCP 6(2):27–279

16.

Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416MathSciNetCrossRef

17.

Ester M, Kriegel HP, Xu X, Sanders J (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: International conference on knowledge discovery and data mining

18.

Han J, Kamber M (2006) Data mining: concepts and techniques. In: Data mining concepts models methods and, 2nd edn, vol 5, no 4, pp 1–18

19.

Xu R, Wunsch DC (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678CrossRef

20.

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

21.

Pizzagalli Diego Ulisse, Gonzalez Santiago F, Krause Rolf (2019) A trainable clustering algorithm based on shortest paths from density peaks. Sci Adv 5(10):eaax3770CrossRef

22.

Xie J, Gao H, Xie W, Liu X, Grant PW (2016) Robust clustering by detecting density peaks and assigning points based on fuzzy weighted k-nearest neighbors. Inf Sci 354:19–40CrossRef

23.

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

24.

Liu R, Wang H, Yu X (2018) Shared-nearest-neighbor-based clustering by fast search and find of density peaks. Inf Sci 450:200–226MathSciNetCrossRef

25.

Jain AK, Law MH (2005) Data clustering: a user’s dilemma. In: International conference on pattern recognition and machine intelligence, pp 1–10

26.

Ball GH, Hall DJ (1965) ISODATA, a novel method of data analysis and pattern classification. Stanford Research Iinst, Menlo Park CA

27.

Chang H, Yeung D-Y (2008) Robust path-based spectral clustering. Pattern Recognit 41(1):191–203CrossRef

28.

Zahn CT (1971) Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput 100(1):68–86CrossRef

29.

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

30.

Gionis A, Mannila H, Tsaparas P (2007) Clustering aggregation. ACM Trans Knowl Discov Data 1(1):4CrossRef

31.

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

32.

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

33.

L Zelnikmanor, P Perona (2004) Self-tuning spectral clustering. Neural Inf Process Syst

34.

Vinh HX, Epps J, Bailey J (2010) Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J Mach Learn Res 11(1):2837–2854MathSciNetMATH

35.

Fowlkes EB, Mallows CL (1983) A method for comparing two hierarchical clusterings. J Am Stat Assoc 78(383):553–569CrossRef

36.

Franti Pasi, Virmajoki Olli, Hautamaki Ville (2006) Fast agglomerative clustering using a k-nearest neighbor graph. IEEE Trans Pattern Anal Mach Intell 28(11):1875–1881CrossRef

37.

Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of the second IEEE workshop on applications of computer vision, pp 138–142

Titel: A novel clustering algorithm by adaptively merging sub-clusters based on the Normal-neighbor and Merging force
verfasst von: Guan Junyi
Sheng li
He Xiongxiong
Chen Jiajia
Publikationsdatum: 12.05.2021
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2021
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-021-00981-1

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