Research and application of clustering algorithm for arbitrary data set

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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
Algorithms
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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