PHC: a fast partition and hierarchy-based clustering algorithm

Authors:
HaoFeng Zhou;QingQing Yuan;ZunPing Cheng;BaiLe Shi
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai 200433, P.R. China;Department of Computing and Information Technology, Fudan University, Shanghai 200433, P.R. China;Department of Computing and Information Technology, Fudan University, Shanghai 200433, P.R. China;Department of Computing and Information Technology, Fudan University, Shanghai 200433, P.R. China
Venue:
Journal of Computer Science and Technology
Year:
2003

Citing 6
Cited 0

BIRCH: an efficient data clustering method for very large databases

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques

Data mining: concepts and techniques
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ROCK: A Robust Clustering Algorithm for Categorical Attributes

ICDE '99 Proceedings of the 15th International Conference on Data Engineering

Quantified Score

Hi-index	0.00

Visualization

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.