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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 8/2018

02.03.2017 | Original Article

Density peaks clustering using geodesic distances

verfasst von: Mingjing Du, Shifei Ding, Xiao Xu, Yu Xue

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 8/2018

Einloggen

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

search-config
loading …

Abstract

Density peaks clustering (DPC) algorithm is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, it cannot effectively group data with arbitrary shapes, or multi-manifold structures. To handle this drawback, we propose a new density peaks clustering, i.e., density peaks clustering using geodesic distances (DPC-GD), which introduces the idea of the geodesic distances into the original DPC method. By experiments on synthetic data sets, we reveal the power of the proposed algorithm. By experiments on image data sets, we compared our algorithm with classical methods (kernel k-means algorithm and spectral clustering algorithm) and the original algorithm in accuracy and NMI. Experimental results show that our algorithm is feasible and effective.
Vorheriger Artikel Multi-label feature selection via feature manifold learning and sparsity regularization
Nächster Artikel Dictionary learning with the -regularizer and the coherence penalty and its convergence analysis

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.
Wen X, Shao L, Xue Y et al (2015) A rapid learning algorithm for vehicle classification. Inf Sci 295:395–406CrossRef
2.
Iam-On N, Boongoen T, Kongkotchawan N (2014) A new link-based method to ensemble clustering and cancer microarray data analysis. Int J Collab Intell 1(1):45–67
3.
Jia H, Ding S, Du M et al (2016) Approximate normalized cuts without Eigen-decomposition. Inf Sci 374:135–150CrossRef
4.
Zheng Y, Jeon B, Xu D et al (2015) Image segmentation by generalized hierarchical fuzzy C-means algorithm. J Intell Fuzzy Syst 28(2):961–973
5.
Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufman, San FranciscoMATH
6.
Zhang Y, Sun X, Wang B (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Commun 13(7):16–23CrossRef
7.
Li X, Liang Y, Cai Y (2016) CC-K-means: a candidate centres-based K-means algorithm for text data. Int J Collab Intell 1(3):189–204
8.
Dong CR, Ng WWY, Wang XZ et al (2014) An improved differential evolution and its application to determining feature weights in similarity-based clustering. Neurocomputing 146:95–103CrossRef
9.
Xu L, Ding S, Xu X et al (2016) Self-adaptive extreme learning machine optimized by rough set theory and affinity propagation clustering. Cognit Comput 8(4):720–728CrossRef
10.
Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Sci 344(6191):1492–1496CrossRef
11.
Chen GJ, Zhang XY, Wang ZJ et al (2015) Robust support vector data description for outlier detection with noise or uncertain data. Knowl-Based Syst 90:129–137CrossRef
12.
Lu KY, Xia SY, Xia C (2015) Clustering based road detection method. In: Proceedings of the 34th Chinese Control Conference (CCC). pp 3874–3879
13.
Xie K, Wu J, Yang W, Sun CY (2015) K-means clustering based on density for scene image classification. In: Proceedings of the 2015 Chinese Intelligent Automation Conference. pp 379–386
14.
15.
Zhang Y, Xia Y, Liu Y et al (2015) Clustering sentences with density peaks for multi-document summarization. In: Proceedings of human language technologies: the 2015 annual conference of the north american chapter of the ACL. pp 1262–1267
16.
Tang GH, Jia S, Li J (2015) An enhanced density peak-based clustering approach for hyperspectral band selection. In: Proceedings of the international geoscience and remote sensing symposium. pp 1116–1119
17.
18.
19.
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
20.
Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Sci 290(5500):2319–2323CrossRef
21.
Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Sci 290(5500):2323–2326CrossRef
22.
Liu Z, Wang W, Jin Q et al (2016) Manifold alignment using discrete surface Ricci flow. CAAI Trans Intell Technol 1(3):285–292CrossRef
23.
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefMATH
24.
Sampat MP, Wang Z, Gupta S et al (2009) Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process 18(11):2385–2401MathSciNetCrossRefMATH
25.
Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7:2399–2434MathSciNetMATH
26.
Ding SF, Hua XP (2014) Recursive least squares projection twin support vector machines for nonlinear classification. Neurocomputing 130:3–9CrossRef
27.
Xu X, Law R, Chen W et al (2016) Forecasting tourism demand by extracting fuzzy Takagi–Sugeno rules from trained SVMs. CAAI Trans Intell Technol 1(1):30–42CrossRef
28.
Chen WJ, Shao YH, Hong N (2014) Laplacian smooth twin support vector machine for semi-supervised classification. Int J Mach Learn Cybern 5(3):459–468CrossRef
29.
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Proc Adv Neural Inf Process Syst 2:849–856
30.
Wang L, Bo LF, Jiao LC (2007) Density-sensitive spectral clustering. Acta Electron Sin 35(8):1577–1581
31.
Zelnik-Manor L, Perona P (2004) Self-tuning spectral clustering. Proc Adv Neural Inf Process Syst 1601–1608
32.
Nene SA, Nayar SK, Murase H (1996) Columbia object image library (COIL-20). Technical Report CUCS-005-96. Columbia University, USA
33.
Graham DB, Allinson NM (1998) Characterising virtual eigensignatures for general purpose face recognition. Face recognition. Springer, Berlin Heidelberg, pp 446–456
34.
Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer, BerlinMATH
35.
Ma Z, Liu Q, Sun K et al (2016) A syncretic representation for image classification and face recognition. CAAI Trans Intell Technol 1(2):173–178CrossRef
36.
Zeng S, Yang X, Gou J et al (2016) Integrating absolute distances in collaborative representation for robust image classification. CAAI Trans Intell Technol 1(2):189–196CrossRef
37.
Xia Z, Wang X, Sun X et al (2016) Steganalysis of LSB matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962CrossRef
38.
Gu B, Sheng VS, Wang Z et al (2015) Incremental learning for ν-support vector regression. Neural Netw 67:140–150CrossRef
39.
Jia HJ, Ding SF, Meng LH et al (2014) A density-adaptive affinity propagation clustering algorithm based on spectral dimension reduction. Neural Comput Applic 25(7–8):1557–1567CrossRef
40.
Wang XZ, He YL, Wang DD (2014) Non-naive bayesian classifiers for classification problems with continuous attributes. IEEE Trans Cybern 44(1):21–39CrossRef
41.
Chen WY, Song YQ, Bai HJ et al (2011) Parallel spectral clustering in distributed systems. IEEE Trans Pattern Anal Mach Intell 33(3):568–586CrossRef
42.
Papadimitriou CH, Steiglitz K (1998) Combinatorial optimization: algorithms and complexity. Courier Dover Publications, MineolaMATH
43.
Strehl A, Ghosh J (2003) Cluster ensembles- knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583–617MathSciNetMATH
Metadaten
Titel
Density peaks clustering using geodesic distances
verfasst von
Mingjing Du
Shifei Ding
Xiao Xu
Yu Xue
Publikationsdatum
02.03.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 8/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0648-x

Weitere Artikel der Ausgabe 8/2018

International Journal of Machine Learning and Cybernetics 8/2018 Zur Ausgabe

Original Article

Robust filtering for uncertain discrete-time stochastic neural networks with Markovian jump and mixed time-delays

Original Article

Normalized projection approach to group decision-making with hybrid decision information

Original Article

Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller

Original Article

Exponential operational laws and new aggregation operators of intuitionistic Fuzzy information based on Archimedean T-conorm and T-norm

Original Article

Local descriptor margin projections (LDMP) for face recognition

Original Article

Multi-label feature selection via feature manifold learning and sparsity regularization

Neuer Inhalt

    Bildnachweise