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MWPCA-ICURD: density-based clustering method discovering specific shape original features

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Abstract

Uncertain data exist in many application fields, and there are numerous recent efforts in processing uncertain data to get more reliable results, especially uncertainty processing in clustering method. However, it is one of the urgent challenges to discover clusters with specific shape features. So we present a clustering method for data with uncertainties, and it is called multivariate wavelet principal component analysis-improved clustering using references and density (MWPCA-ICURD), which utilizes feature extraction and density-based clustering. To cluster uncertain data with specific shape original features, the original features are extracted by MWPCA method, which combines digital wavelet decomposition and principal component analysis organically. Then, a density-based clustering method ICURD is explored to discover specific shape clusters. Experimental results illustrate its validation and feasibility.

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Abbreviations

CURD:

Clustering using references and density

ICURD:

Improved clustering using references and density

WSNs:

Wireless sensor networks

DBSCAN:

Density-based spatial clustering of applications with noise

MWPCA:

Multivariate wavelet principal component analysis

PDF:

Probability density function

PCA:

Principal component analysis

MSPCA:

Multiscale wavelet principal component analysis

MSPCA-DBSCAN:

Multiscale wavelet principal component analysis-density-based spatial clustering of applications with noise

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Acknowledgments

The research presented in this paper is supported by National Natural Science Foundation of China (61102038), Natural Science Foundation of Shandong Province of China (ZR2015FM027), Space support technology fund Projects (2014-HT-HGD5), Research Fund of Harbin institute of technology (WeiHai) [HIT (WH) 201306, HIT (WH) 201307], Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ14205,YQ15203), Natural Scientific Research Innovation Foundation in Harbin Institute of Technology (HIT.NSRIF.2015122) and State Key Laboratory of Geo-information Engineering (SKLGIE2014-M-2-4).

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Harbin Institute of Technology at WeiHai, No. 2 WenHua west road, WeiHai, 264209, ShanDong Province, China

    Qinghua Luo

  2. GuangXi Key Laboratory of Automatic Detecting Technology and Instruments (GuiLin University of Electronic Technology), GuiLin, GuangXi Province, China

    Qinghua Luo

  3. State Key Laboratory of Geo-information Engineering, Xi’an, ShanXi Province, China

    Qinghua Luo

  4. State Key Laboratory of Satellite Navigation Engineering Technology, ShiJiaZhuang, HeBei Province, China

    Qinghua Luo

  5. Automatic Test and Control Institute, Harbin Institute of Technology, Harbin, 150080, HeiLongJiang province, China

    Yu Peng, Junbao Li & Xiyuan Peng

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  1. Qinghua Luo
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  2. Yu Peng
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  3. Junbao Li
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  4. Xiyuan Peng
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Correspondence to Qinghua Luo.

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Luo, Q., Peng, Y., Li, J. et al. MWPCA-ICURD: density-based clustering method discovering specific shape original features. Neural Comput & Applic 28, 2545–2556 (2017). https://doi.org/10.1007/s00521-016-2208-9

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  • DOI: https://doi.org/10.1007/s00521-016-2208-9

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