Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 6/2020

13.11.2019 | Original Article

Mk-NNG-DPC: density peaks clustering based on improved mutual K-nearest-neighbor graph

verfasst von: Jian-cong Fan, Pei-ling Jia, Linqiang Ge

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Clustering by fast search and detection of density peaks (DPC, Density Peaks Clustering) is a relatively novel clustering algorithm published in the Science journal. As a density-based clustering algorithm, DPC produces better clustering results while using less parameters than other relevant algorithms. However, we found that the DPC algorithm does not perform well if clusters with different densities are very close. To address this problem, we propose a new DPC algorithm by incorporating an improved mutual k-nearest-neighbor graph (Mk-NNG) into DPC. Our Mk-NNG-DPC algorithm leverages the distance matrix of data samples to improve the Mk-NNG, and then utilizes DPC to constrain and select cluster centers. The proposed Mk-NNG-DPC algorithm ensures an instance to be allocated to the fittest cluster. Experimental results on synthetic and real world datasets show that our Mk-NNG-DPC algorithm can effectively and efficiently improve clustering performance, even for clusters with arbitrary shapes.
Vorheriger Artikel Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors
Nächster Artikel A global and local feature weighted method for ancient murals inpainting

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann, BurlingtonMATH
2.
Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New YorkMATH
3.
Chifu AG, Hristea F, Mothe J, Popescu M (2015) Word sense discrimination in information retrieval: a spectral clustering-based approach. Inf Process Manag 51(2):16–31
4.
Kaufman L, Rousseeuw P (2009) Finding groups in data: an introduction to cluster analysis. Wiley, HobokenMATH
5.
Kearns M, Mansour Y, Ng AY (1999) An information-theoretic analysis of hard and soft assignment methods for clustering. In: Jordan MI (ed) Learning in graphical models. MIT Press, Cambridge, pp 495–520
6.
Forgy EW (1965) Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics 21:768–769
7.
Bishnu PS, Bhattacherjee V (2013) A modified K-modes clustering algorithm. pattern recognition and machine intelligence, Volume 8251 of the series Lecture Notes in Computer Science, 2013, pp 60–66
8.
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of ACM SIGMOD international conference on management of data, 1996, pp 103–114
9.
Karypis G, Han E, Kumar V (1999) CHAMELEON: a hierarchical clustering algorithm using dynamic modeling. IEEE Comput 32(8):68–75
10.
11.
Ester M, Kriegel H, Sander J, Xu X, Simoudis E, Han J, Fayyad UM (eds) (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining (KDD-96). AAAI Press, pp 226–231
12.
Hinneburg A, Gabriel HH (2007) DENCLUE 2.0: fast clustering based on kernel density estimation. In: Proceedings of the 2007 international conference on intelligent data analysis (IDA’07), Ljubljana, Slovenia, 2007, pp 70–80
13.
Banerjee A, Shan H (2010) Model-based clustering. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning, pp 686–689
14.
Ding S, Zhang N, Zhang J, Xu X, Shi Z (2017) Unsupervised extreme learning machine with representational features. Int J Mach Learn Cybern 8(2):587–595
15.
16.
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496
17.
Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972–976MathSciNetMATH
18.
Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619
19.
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905
20.
Arias-Castro E, Chen G, Lerman G (2011) Spectral clustering based on local linear approximations. Electron J Stat 5(1):1537–1587MathSciNetMATH
21.
Székely GJ, Rizzo ML (2005) Hierarchical clustering via Joint between-within distances: extending ward’s minimum variance method. J Classif 22(2):151–183MathSciNetMATH
22.
Figueiredo MAT, Jain AK (2002) Unsupervised learning of finite mixture models. IEEE Trans Pattern Anal Mach Intell 24(3):381–396
23.
McLachlan GJ, Peel D (2000) Finite mixture models. Wiley, New JerseyMATH
24.
25.
Nayak J, Naik B, Behera HS (2014) Fuzzy C-means (FCM) clustering algorithm: a decade review from 2000 to 2014. Computational Intelligence in Data Mining-Volume 2, Volume 32 of the series Smart Innovation, Systems and Technologies, pp 133–149
26.
Brito MR, Chávez EL, Quiroz AJ, Yukich JE (1997) Connectivity of the mutual k-nearest-neighbor graph in clustering and outlier detection. Stat Probab Lett 35(1):33–42MathSciNetMATH
27.
Sardana D, Bhatnagar R (2014) Graph clustering using mutual K-nearest neighbors. Active Media Technology, Volume 8610 of the series Lecture Notes in Computer Science, pp 35–48
28.
Xie J, Gao H, Xie W (2016) K-nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a dataset. Sci Sin Inf 46(2):258–280
29.
30.
Cover TM, Thomas JA (2001) Elements of information theory. Wiley, HobokenMATH
31.
Fan J, Niu Z, Liang Y, Zhao Z (2016) Probability model selection and parameter evolutionary estimation for clustering imbalanced data without sampling. Neurocomputing 211(10):172–181
32.
Wang F, Zhang C (2005) Spectral clustering for time series. In: Proceedings of third international conference on advances in pattern recognition, ICAPR 2005, Bath, UK, August 22–25, 2005, pp 345–354
33.
Xu X, Ding S, Du M, Xue Y (2018) DPCG: an efficient density peaks clustering algorithm based on grid. Int J Mach Learn Cybern 9(5):743–754
34.
Du M, Ding S, Xue Y (2018) A robust density peaks clustering algorithm using fuzzy neighborhood. Int J Mach Learn Cybern 9(7):1131–1140
35.
Bai X, Yang P, Shi X (2017) An overlapping community detection algorithm based on density peaks. Neurocomputing 226(2):7–15
36.
Campello RJGB, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. In: Pei J, Tseng VS, Cao L, Motoda H, Xu G (eds) Advances in knowledge discovery and data mining. Lecture notes in computer science. Springer, Berlin Heidelberg, pp 160–172
37.
Li J, Huang X, Selke C, Yong J (2007) A fast algorithm for finding correlation clusters in noise data. In: Proceedings of the 11th Pacific-Asia conference on knowledge discovery and data mining, pp 639–647
38.
Zhang T-T, Yuan B (2018) Density-based multiscale analysis for clustering in strong noise settings with varying densities. IEEE Access 6:25861–25873
39.
Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318MathSciNet
40.
Wang X, Xing H-J, Li Y et al (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1654
41.
Wang R, Wang X, Kwong S, Chen X (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475
42.
Wang X, Wang R, Chen X (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715
43.
Wang X, Zhang T, Wang R (2019) Non-iterative deep learning: incorporating restricted boltzmann machine into multilayer random weight neural networks. IEEE Trans Syst Man Cybern Syst 49(7):1299–1380
44.
Lin JCW, Yang L, Fournier-Viger P, Hong TP (2018) Mining of skyline patterns by considering both frequent and utility constraints. Eng Appl Artif Intell 77:229–238
45.
Fournier-Viger P, Lin JCW, Kiran RU, Koh YS, Thomas R (2017) A survey of sequential pattern mining. Data Sci Pattern Recognit 1(1):54–77
46.
Chen CM, Xiang B, Liu Y, Wang KH (2019) A secure authentication protocol for internet of vehicles. IEEE ACCESS 7(1):12047–12057
47.
Chen CM, Xiang B, Wang KH, Yeh KH, Wu TY (2018) A robust mutual authentication with a key agreement scheme for session initiation protocol. Appl Sci 8(10):1
48.
Yang C, Huang L, Li F (2018) Exponential synchronization control of discontinuous non-autonomous networks and autonomous coupled networks. Complexity 1:1–10
49.
Lian D, Xianwen F, Chuangxia H (2017) Global exponential convergence in a delayed almost periodic nicholsons blowflies model with discontinuous harvesting. Math Methods Appl Sci 41(5):1954–1965MathSciNetMATH
50.
Lian D, Lihong H, Zhenyuan G (2017) Periodic attractor for reactiondiffusion high-order hopfield neural networks with time-varying delays. Comput Math Appl 73(2):233–245MathSciNetMATH
51.
Huang C, Liu B, Tian X, Yang L, Zhang X (2019) Global convergence on asymptotically almost periodic SICNNs with nonlinear decay functions. Neural Process Lett 49(2):625–641
52.
Huang C, Zhang H, Huang L (2019) Almost periodicity analysis for a delayed Nicholson’s blowflies model with nonlinear density-dependent mortality term. Commun Pure Appl Anal 18(6):3337–3349MathSciNet
53.
Huang C, Zhang H (2019) Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method. Int J Biomath 12(02):1950016MathSciNetMATH
54.
Huang C, Cao J, Wen F, Yang X (2016) Stability analysis of SIR model with distributed delay on complex networks. PLoS One 11(8):e0158813
55.
Li Y, Fan JC, Pan JS, Mao GH, Wu GK (2019) A novel rough fuzzy clustering algorithm with a new similarity measurement. J Internet Technol 20(4):1
56.
Fan J-C, Li Y, Tang Lei-Yu, Geng-Kun W (2018) RoughPSO: rough set-based particle swarm optimisation. Int J Bio-Inspired Comput 12(4):245–253
Metadaten
Titel
Mk-NNG-DPC: density peaks clustering based on improved mutual K-nearest-neighbor graph
verfasst von
Jian-cong Fan
Pei-ling Jia
Linqiang Ge
Publikationsdatum
13.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2020
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-01031-3

Weitere Artikel der Ausgabe 6/2020

International Journal of Machine Learning and Cybernetics 6/2020 Zur Ausgabe

Original Article

Study on the prediction of stock price based on the associated network model of LSTM

Original Article

Selecting park locations using a genetic algorithm and comprehensive satisfaction

Original Article

Integrating MTS with bagging strategy for class imbalance problems

Original Article

A self-adaptive preference model based on dynamic feature analysis for interactive portfolio optimization

Original Article

Drug sensitivity prediction framework using ensemble and multi-task learning

Original Article

Sparse graphs using global and local smoothness constraints

Neuer Inhalt

    Bildnachweise