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

MWPCA-ICURD: density-based clustering method discovering specific shape original features 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

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

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Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2208-9
Publisher site
See Article on Publisher Site

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.

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Jan 29, 2016

References

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