Clustering Algorithm incorporating Density and Direction
Submitted by kzhang on Tue, 21/04/2009 - 21:31.
| Publication Type | Conference Paper | |
| Year of Publication | 2008 | |
| Authors | Song, Y.; O'Grady, M.J.; O'Hare, G.M.P.; Wang, W.A. | |
| Conference Name | CIMCA 2008 - Proceedings of the 2008 International Conference on Computational Intelligence for Modelling, Control and Automation | |
| Conference Date | 10-12 Dec 2008 | |
| Publisher | IEEE Computer Society | |
| Conference Location | Vienna, Austria | |
| ISBN Number | 978-0-7695-3514-2 | |
| Key Words | RP3 | |
| Abstract | This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm – Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design of the CADD algorithm. As an illustration of its applicability, CADD was used to cluster real world data from the geochemistry domain. | |
| URL | http://irserver.ucd.ie/dspace/handle/10197/1346 | |
| DOI | 10.1109/CIMCA.2008.34 |
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