Experimental research on impacts of dimensionality on clustering algorithms

Download

Full text for this resource is not available from the Research Repository.

Export

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).

Dimensions Badge

Altmetric Badge

Item type	Book Section
URI	https://vuir.vu.edu.au/id/eprint/9970
DOI	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
Download/View statistics	View download statistics for this item