Assessing the performance of a graph-based clustering algorithm

Authors:
Pasquale Foggia;Gennaro Percannella;Carlo Sansone;Mario Vento
Affiliations:
Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Napoli, Italy;Dipartimento di Ingegneria dell'Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, SA, Italy;Dipartimento di Informatica e Sistemistica, Università di Napoli Federico II, Napoli, Italy;Dipartimento di Ingegneria dell'Informazione ed Ingegneria Elettrica, Università di Salerno, Fisciano, SA, Italy
Venue:
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Year:
2007

Citing 12
Cited 3

Algorithms for clustering data

Algorithms for clustering data
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps

Self-organizing maps
Data clustering: a review

ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Fundamentals of Computer Alori

Fundamentals of Computer Alori
An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Evaluation of Some Clustering Algorithms and Validity Indices

IEEE Transactions on Pattern Analysis and Machine Intelligence
Validation indices for graph clustering

Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
On clusterings-good, bad and spectral

FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters

IEEE Transactions on Computers

Benchmarking graph-based clustering algorithms

Image and Vision Computing
Clustering with r-regular graphs

Pattern Recognition
An effective graph-based clustering technique to identify coherent patterns from gene expression data

International Journal of Bioinformatics Research and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Graph-based clustering algorithms are particularly suited for dealing with data that do not come from a Gaussian or a spherical distribution. They can be used for detecting clusters of any size and shape without the need of specifying the actual number of clusters; moreover, they can be profitably used in cluster detection problems. In this paper, we propose a detailed performance evaluation of four different graph-based clustering approaches. Three of the algorithms selected for comparison have been chosen from the literature. While these algorithms do not require the setting of the number of clusters, they need, however, some parameters to be provided by the user. So, as the fourth algorithm under comparison, we propose in this paper an approach that overcomes this limitation, proving to be an effective solution in real applications where a completely unsupervised method is desirable.