Experimental research on impacts of dimensionality on clustering algorithms

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Meng, Hai-Dong, Ma, Jin-Hui and Xu, Guandong (2010) Experimental research on impacts of dimensionality on clustering algorithms. In: 2010 International Conference on Computational Intelligence and Software Engineering (CISE 2010) : December 10-12, 2010, Wuhan, China. IEEE, Piscataway, N.J..

Abstract

Experiments are carried out on datasets with different dimensions selected from UCI datasets by using two classical clustering algorithms. The results of the experiments indicate that when the dimensionality of the real dataset is less than or equal to 30, the clustering algorithms based on distance are effective. For high-dimensional datasets--dimensionality is greater than 30, the clustering algorithms are of weaknesses, even if we use dimension reduction methods, such as Principal Component Analysis (PCA).

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Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9970
DOI https://doi.org/10.1109/CISE.2010.5677260
Official URL http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arn...
ISBN 9781424453917 (print), 9781424453924 (online)
Subjects Historical > FOR Classification > 0103 Numerical and Computational Mathematics
Historical > FOR Classification > 0807 Library and Information Studies
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID21673, accuracy, algorithm design and analysis, classification algorithms, data mining, partitioning algorithms, principal component analysis, UCI dataset, dimension reduction method, dimensionality, high-dimensional dataset, principal component analysis
Citations in Scopus 0 - View on Scopus
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