Skip to main content
SpringerLink
Log in

A new local density and relative distance based spectrum clustering

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

A novel local density and relative distance-based spectrum clustering (LDRDSC) algorithm is proposed for multidimensional data clustering. The density spectra consider both redefined local densities and relative distances. The spectral peaks are defined as cluster centers since these peaks correspond to the local density maximums. Different clusters correspond to different spectra. The clustering by fast search and find of density peaks (CFSFDP) algorithm and several benchmark data sets are employed to validate our proposed LDRDSC algorithm. Once the density spectrum is generated, the rest points can be automatically clustered by our LDRDSC algorithm, which is different from CFSFDP. CFSFDP needs to categorize data points according to the cluster centers. Furthermore, our LDRDSC algorithm is compared with other five typical clustering algorithms (DBSCAN, FCM, AP, Mean Shift and k-means) in order to validate the effectiveness of the proposed algorithm. Computational results demonstrate that our algorithm can obtain a better clustering result than the above mentioned algorithms, especially in identifying noises or isolates.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Frigui H, Krishnapuram R (1999) A robust competitive clustering algorithm with applications in computer vision. IEEE Trans Pattern Anal Mach Intell 21(5):450–465

    Article  Google Scholar 

  2. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Sufisstrunk S (2012) Sliced superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282

    Article  Google Scholar 

  3. Elhamifar E, Vidal R (2009) Sparse subspace clustering. In: IEEE conference on computer vision and pattern recognition, CVPR, pp 2790–2797

  4. 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 

  5. King AD, Prulj N, Jurisica I (2004) Protein complex prediction via cost-based clustering. Bioinformatics 20(17):3013–3020

    Article  Google Scholar 

  6. Huang DW, Sherman BT, Lempicki RA (2008) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4(1):44–57

    Article  Google Scholar 

  7. Moosmann F, Nowak E, Jurie F (2008) Randomized clustering forests for image classification. IEEE Trans Pattern Anal Mach Intell 30(9):1632–1646

    Article  Google Scholar 

  8. Ducournau A, Bretto A, Rital S, Laget B (2012) A reductive approach to hypergraph clustering: an application to image segmentation. Pattern Recognit 45(7):2788–2803

    Article  MATH  Google Scholar 

  9. Chaira T (2011) A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl Soft Comput 11(2):1711–1717

    Article  Google Scholar 

  10. Wang R, Ji W, Liu M, Wang X, Weng J, Deng S, Gao S, Yuan C (2018) Review on mining data from multiple data sources. Pattern Recognit Lett. https://doi.org/10.1016/j.patrec.2018.01.013

  11. Wu J, Jin L, Liu M (2015) Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148(2):136–142

    Article  Google Scholar 

  12. Sunita AR, Jalal Anand S, Kumar JM (2010) A density based algorithm for discovering density varied clusters in large spatial databases. Int J Comput Appl 3(6):1–4

    Google Scholar 

  13. Hinneburg A, Gabriel H-H (2007) DENCLUE 2.0: fast clustering based on kernel density estimation. Adv Intell Data Anal VII Lect Notes Comput Sci 4723:70–80

    Article  Google Scholar 

  14. Ester M, Kriegel H, Sander J, Xu X (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, AAAI Press, Oregon, pp 226–231

  15. Sander J, Ester M, Kriegel H, Xu X (1998) Density-based clustering in spatial data sets: the algorithm GDBSCAN and its applications. Data Min Knowl Disc 2:169–194

    Article  Google Scholar 

  16. Ankerst M, Breunig MM, Kriegel HP, Sander J (1999) OPTICS, ordering points to identify the clustering structure. In: ACM SIGMOD international conference on management of data, pp 49–60

  17. Xu X, Jager J, Kriegel H (1999) A fast parallel clustering algorithm for large spatial databases. Data Min Knowl Disc 3(3):263–290

    Article  Google Scholar 

  18. Zaiane O, Lee C (2002) Clustering spatial data in the presence of obstacles: a density-based approach. In: Proceedings of the IEEE symposium on international database engineering and applications, Edmonton, Canada, pp 214–223

  19. Dash M, Liu H, Xu X (2001) ‘\(1+1 > 2\)’: merging distance and density based clustering. In: Proceedings of the seventh international conference on database systems for advanced applications, IEEE, Hong Kong, pp 32–39

  20. Nasibov E, Ulutagay G (2009) Robustness of density-based clustering methods with various neighborhood relations. Fuzzy Sets Syst 160(24):3601–3615

    Article  MathSciNet  MATH  Google Scholar 

  21. Kieu L-M, Bhaskar A, Chung E (2015) A modified density-based scanning algorithm with noise for spatial travel pattern analysis from smart card AFC data. Trans Res Part C 58:193–207

    Article  Google Scholar 

  22. Maadi AE, Djouadi MS (2015) Using a light DBSCAN algorithm for visual surveillance of crowded traffic scenes. IETE J Res 61(3):308–320

    Article  Google Scholar 

  23. Chen X (2015) A new clustering algorithm based on near neighbor influence. Exp Syst Appl 42:7746–7758

    Article  Google Scholar 

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

    Article  Google Scholar 

  25. Liu P, Zhou D, Wu N (2007) VDBSCAN: varied density based spatial clustering of application with noise. In: Proceedings of the IEEE international conference on service systems and service management, Chengdu, pp 528–531

  26. Hinneburg A, Keim D (1998) An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the fourth international conference on knowledge discovery and data mining, New York, pp 58–65

  27. Ma D, Zhan A (2004) An adaptive density-based clustering algorithm for spatial database with noise. In: Proceedings of the fourth IEEE international conference on data mining, Brighton, UK, pp 467–470

  28. Gupta G, Liu A, Ghosh J (2010) Automated hierarchical density shaving: a robust automated clustering and visualization framework for large biological data sets. IEEE/ACM Trans Comput Biol Bioinform 7(2):223–237

    Article  Google Scholar 

  29. Huang J, Sun H, Song Q, Deng H, Han J (2013) Revealing density-based clustering structure from the core-connected tree of a network. IEEE Knowl Data Eng 25(8):1876

    Article  Google Scholar 

  30. Li X, Ceikute V, Jensen CS, Tan K-L (2013) Effective online group discovery in trajectory databases. IEEE Knowl Data Eng 25(12):2752

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Yu D, Ma X, Tu Y, Lai L (2015) Both piston-like and rotational motions are present in bacterial chemoreceptor signaling. Scientific Reports. 5, 8640, 02 March 2015

  33. Chen Y-W, Lai D-H, Qi H, Wang J-L, Du J-X (2015) A new method to estimate ages of facial image for large database. Multimed Tools Appl 75:2877. https://doi.org/10.1007/s11042-015-2485-9

    Article  Google Scholar 

  34. Kumar P, Srinivasan B, Mohapatra NR (2015) Fast and accurate lithography simulation using cluster analysis in resist model building. J Micro/Nanolith MEMS MOEMS 14(2):023506

    Article  Google Scholar 

  35. Alcalá-Fdez J, Sánchez L, García S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms to data mining problems. Soft Comput 13(3):307–318. https://doi.org/10.1007/s00500-008-0323-y

    Article  Google Scholar 

  36. Alcalá-Fdez J, Fernandez A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2011) KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Log Soft Comput 17(2–3):255–287

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Foundation of Science, China (41274109), the Innovative Team Project of Sichuan Province (2015TD0020) and the New Zealand Marsden Fund.

Author information

Authors and Affiliations

  1. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, China

    Mingzhe Liu & Mingfu He

  2. College of Sciences, Massey University, Auckland, New Zealand

    Ruili Wang

  3. College of Earth Science, Chengdu University of Technology, Chengdu, 610059, China

    Shaoda Li

Authors
  1. Mingzhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingfu He
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruili Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shaoda Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ruili Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, M., He, M., Wang, R. et al. A new local density and relative distance based spectrum clustering. Knowl Inf Syst 61, 965–985 (2019). https://doi.org/10.1007/s10115-018-1316-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-018-1316-5

Keywords

Search

Navigation