Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CSSE.2008.415 | CSSE (4) |
Keywords | Field | DocType |
clustering result,geological domain,pattern clustering,clustering algorithm,object direction,denclue algorithm,cadd,clustering analysis,arbitrary data set,algorithmic design,respective advantage,simple data,k-means algorithm,object density,multiple shape,number k,cadd algorithm,cluster analysis,algorithm design,k means,noise measurement,clustering algorithms,computer science,software engineering,k means algorithm | Data mining,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Affinity propagation,Correlation clustering,Computer science,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISBN | Citations |
4 | 978-0-7695-3336-0 | 1 |
PageRank | References | Authors |
0.44 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuchen Song | 1 | 4 | 3.33 |
| M.J. O’Grady | 2 | 209 | 19.33 |
| G. M. P. O'Hare | 3 | 370 | 32.35 |