A Graph-Theoretic Approach to Nonparametric Cluster Analysis

Authors:
W. L. G. Koontz;P. M. Narendra;K. Fukunaga
Affiliations:
Bell Laboratories;-;-
Venue:
IEEE Transactions on Computers
Year:
1976

Citing 4
Cited 17

Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers
A Branch and Bound Algorithm for Computing k-Nearest Neighbors

IEEE Transactions on Computers
An Algorithm for Detecting Unimodal Fuzzy Sets and Its Application as a Clustering Technique

IEEE Transactions on Computers
A Nonparametric Valley-Seeking Technique for Cluster Analysis

IEEE Transactions on Computers

Recent Developments in Pattern Recognition

IEEE Transactions on Computers
Effective image retrieval using dominant color descriptor and fuzzy support vector machine

Pattern Recognition
MAkE: Multiobjective algorithm for k-way equipartitioning of a point set

Applied Soft Computing
Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures

Expert Systems with Applications: An International Journal
Genetic algorithm for text clustering based on latent semantic indexing

Computers & Mathematics with Applications
Parametric and nonparametric evolutionary computing with a content-based feature selection approach for parallel categorization

Expert Systems with Applications: An International Journal
An investigation of mountain method clustering for large data sets

Pattern Recognition
A tree-structured framework for purifying "complex" clusters with structural roles of individual data

Pattern Recognition
Distributed data clustering in multi-dimensional peer-to-peer networks

ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Supervised and unsupervised clustering with probabilistic shift

ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Fuzzy evolutionary optimization modeling and its applications to unsupervised categorization and extractive summarization

Expert Systems with Applications: An International Journal
Persistence-based clustering in riemannian manifolds

Proceedings of the twenty-seventh annual symposium on Computational geometry
A fast directed tree based neighborhood clustering algorithm for image segmentation

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval

Computers and Electrical Engineering
Mode seeking clustering by KNN and mean shift evaluated

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Persistence-Based Clustering in Riemannian Manifolds

Journal of the ACM (JACM)
Automatic Topic Ontology Construction Using Semantic Relations from WordNet and Wikipedia

International Journal of Intelligent Information Technologies

Quantified Score

Hi-index	14.99

Visualization

Abstract

Nonparametric clustering algorithms, including mode-seeking, valley-seeking, and unimodal set algorithms, are capable of identifying generally shaped clusters of points in metric spaces. Most mode and valley-seeking algorithms, however, are iterative and the clusters obtained are dependent on the starting classification and the assumed number of clusters. In this paper, we present a noniterative, graph-theoretic approach to nonparametric cluster analysis. The resulting algorithm is governed by a single-scalar parameter, requires no starting classification, and is capable of determining the number of clusters. The resulting clusters are unimodal sets.