Research and application of clustering algorithm for arbitrary data set
|Title:||Research and application of clustering algorithm for arbitrary data set||Authors:||Song, Yu-Chen
O'Grady, Michael J.
O'Hare, G. M. P. (Greg M. P.)
|Permanent link:||http://hdl.handle.net/10197/1347||Date:||Dec-2008||Abstract:||This paper discusses the theory and algorithmic design of the CADD (clustering algorithm based on object density and direction) algorithm. This algorithm seeks to harness the respective advantages of the k-means and DENCLUE algorithms. Clustering results are illustrated using both a simple data set and one from the geological domain. Results indicate that CADD is robust in that automatically determines the number K of clusters, and is capable of identifying clusters of multiple shapes and sizes.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE Computer Society||Copyright (published version):||2008 by The Institute of Electrical and Electronics Engineers, Inc||Subject LCSH:||Cluster analysis--Computer programs
|DOI:||10.1109/CSSE.2008.415||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings : International Conference on Computer Science and Software Engineering : CSSE 2008 : Volume 04||Conference Details:||Paper presented at the 2008 International Conference on Computer Science and Software Engineering, December 12-14, 2008, Wuhan, China|
|Appears in Collections:||CLARITY Research Collection|
Computer Science Research Collection
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