DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points

Authors:
Caiming Zhong;Duoqian Miao;Ruizhi Wang;Xinmin Zhou
Affiliations:
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China and College of Science and Technology, Ningbo University, Ningbo 315211, PR China;School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China and Tongji Branch, National Engineering and Technology Center of High Performance Computer, Shanghai ...;School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China and Tongji Branch, National Engineering and Technology Center of High Performance Computer, Shanghai ...;School of Electronics and Information Engineering, Tongji University, Shanghai 201804, PR China
Venue:
Pattern Recognition Letters
Year:
2008

Citing 15
Cited 2

CURE: an efficient clustering algorithm for large databases

SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data clustering: a review

ACM Computing Surveys (CSUR)
LOF: identifying density-based local outliers

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Outlier detection for high dimensional data

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering validity checking methods: part II

ACM SIGMOD Record
A Maximum Variance Cluster Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Mining Distance-Based Outliers in Large Datasets

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering cluster-based local outliers

Pattern Recognition Letters
A novel genetic algorithm for automatic clustering

Pattern Recognition Letters
Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging

IEEE Transactions on Knowledge and Data Engineering
Efficiently Mining Gene Expression Data via a Novel Parameterless Clustering Method

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Pattern Recognition, Third Edition

Pattern Recognition, Third Edition
DIVCLUS-T: A monothetic divisive hierarchical clustering method

Computational Statistics & Data Analysis

A polythetic clustering process and cluster validity indexes for histogram-valued objects

Computational Statistics & Data Analysis
Improving DBSCAN's execution time by using a pruning technique on bit vectors

Pattern Recognition Letters

Quantified Score

Hi-index	0.10

Visualization

Abstract

Although many clustering methods have been presented in the literature, most of them suffer from some drawbacks such as the requirement of user-specified parameters and being sensitive to outliers. For general divisive hierarchical clustering methods, an obstacle to practical use is the expensive computation. In this paper, we propose an automatic divisive hierarchical clustering method (DIVFRP). Its basic idea is to bipartition clusters repeatedly with a novel dissimilarity measure based on furthest reference points. A sliding average of sum-of-error is employed to estimate the cluster number preliminarily, and the optimum number of clusters is achieved after spurious clusters identified. The method does not require any user-specified parameter, even any cluster validity index. Furthermore it is robust to outliers, and the computational cost of its partition process is lower than that of general divisive clustering methods. Numerical experimental results on both synthetic and real data sets show the performances of DIVFRP.