Neighbor number, valley seeking and clustering

Authors:
Chaolin Zhang;Xuegong Zhang;Michael Q. Zhang;Yanda Li
Affiliations:
Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA and Depar ...;Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China;Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China and Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA;Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, China
Venue:
Pattern Recognition Letters
Year:
2007

Citing 11
Cited 3

Low-level segmentation of multispectral images via agglomerative clustering of uniform neighbourhoods

Pattern Recognition
Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory

The nature of statistical learning theory
Location- and Density-Based Hierarchical Clustering Using Similarity Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Web document clustering: a feasibility demonstration

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Data clustering: a review

ACM Computing Surveys (CSUR)
Information Theoretic Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Knowledge Acquisition Via Incremental Conceptual Clustering

Machine Learning
Distribution Free Decomposition of Multivariate Data

SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Support vector clustering

The Journal of Machine Learning Research
The estimation of the gradient of a density function, with applications in pattern recognition

IEEE Transactions on Information Theory

Fuzzy mode enhancement and detection for color image segmentation

Journal on Image and Video Processing - Color in Image and Video Processing
A document clustering algorithm for discovering and describing topics

Pattern Recognition Letters
The role of hubness in clustering high-dimensional data

PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I

Quantified Score

Hi-index	0.10

Visualization

Abstract

This paper proposes a novel nonparametric clustering algorithm capable of identifying shape-free clusters. This algorithm is based on a nonparametric estimation of the normalized density derivative (NDD) and the local convexity of the density distribution function, both of which are represented in a very concise form in terms of neighbor numbers. We use NDD to measure the dissimilarity between each pair of observations in a local neighborhood and to build a connectivity graph. Combined with the local convexity, this similarity measure can detect observations in local minima (valleys) of the density function, which separate observations in different major clusters. We demonstrate that this algorithm has a close relationship with the single-linkage hierarchical clustering and can be viewed as its extension. The performance of the algorithm is tested with both synthetic and real datasets. An example of color image segmentation is also given. Comparisons with several representative existing algorithms show that the proposed method can robustly identify major clusters even when there are complex configurations and/or large overlaps.