Name
Abstract | ||
|---|---|---|
Cluster analysis is a process to classify data in a specified data set. In this field, much attention is paid to high-efficiency clustering algorithms. In this paper, the features in the current partition-based and hierarchy-based algorithms are reviewed, and a new hierarchy-based algorithm PHC is proposed by combining advantages of both algorithms, which uses the cohesion and the closeness to amalgamate the clusters. Compared with similar algorithms, the performance of PHC is improved, and the quality of clustering is guaranteed. And both the features were proved by the theoretic and experimental analyses in the paper. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/BF02948913 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
data mining,new hierarchy-based algorithm,similar algorithm,fast partition,experimental analysis,specified data,hierarchy-based algorithm,clustering algorithm,hierarchy-based clustering algorithm,cluster analysis | Data mining,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Computer science,Determining the number of clusters in a data set,Cluster analysis,Single-linkage clustering | Journal |
Volume | Issue | ISSN |
18 | 3 | 1860-4749 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haofeng Zhou | 1 | 86 | 7.22 |
| Qingqing Yuan | 2 | 3 | 1.44 |
| Zunping Cheng | 3 | 75 | 6.19 |
| Baile SHI | 4 | 679 | 57.46 |