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Research and application of clustering algorithm for arbitrary data set

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Research and application of clustering algorithm for arbitrary data set
Συγγραφείς: Song, Yu-Chen, O'Grady, Michael J., O'Hare, G. M. P. (Greg M. P.)
Στοιχεία εκδότη: IEEE Computer Society
Έτος έκδοσης: 2009
Συλλογή: University College Dublin: Research Repository UCD
Θεματικοί όροι: Cluster analysis--Computer programs, Algorithms
Περιγραφή: Paper presented at the 2008 International Conference on Computer Science and Software Engineering, December 12-14, 2008, Wuhan, China ; 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. ; Science Foundation Ireland ; Conference details ; http://www.highsci.org/csse2008submission/website/csse/index.aspx
Τύπος εγγράφου: conference object
Περιγραφή αρχείου: 457320 bytes; application/pdf
Γλώσσα: English
ISBN: 978-0-7695-3336-0
0-7695-3336-1
Relation: Proceedings : International Conference on Computer Science and Software Engineering : CSSE 2008 : Volume 04; http://hdl.handle.net/10197/1347
DOI: 10.1109/CSSE.2008.415
Διαθεσιμότητα: http://hdl.handle.net/10197/1347
https://doi.org/10.1109/CSSE.2008.415
Αριθμός Καταχώρησης: edsbas.170FD3EF
Βάση Δεδομένων: BASE
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  Data: Research and application of clustering algorithm for arbitrary data set
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  Data: <searchLink fieldCode="AR" term="%22Song%2C+Yu-Chen%22">Song, Yu-Chen</searchLink><br /><searchLink fieldCode="AR" term="%22O'Grady%2C+Michael+J%2E%22">O'Grady, Michael J.</searchLink><br /><searchLink fieldCode="AR" term="%22O'Hare%2C+G%2E+M%2E+P%2E+%28Greg+M%2E+P%2E%29%22">O'Hare, G. M. P. (Greg M. P.)</searchLink>
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  Data: Paper presented at the 2008 International Conference on Computer Science and Software Engineering, December 12-14, 2008, Wuhan, China ; 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. ; Science Foundation Ireland ; Conference details ; http://www.highsci.org/csse2008submission/website/csse/index.aspx
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