Combining multiple clusterings using similarity graph

Authors:
Selim Mimaroglu;Ertunc Erdil
Affiliations:
Department of Computer Engineering, Bahcesehir University, Ciragan Caddesi, 34353 Besiktas, Istanbul, Turkey;Department of Computer Engineering, Bahcesehir University, Ciragan Caddesi, 34353 Besiktas, Istanbul, Turkey
Venue:
Pattern Recognition
Year:
2011

Citing 17
Cited 5

Algorithms for clustering data

Algorithms for clustering data
Multilevel hypergraph partitioning: application in VLSI domain

DAC '97 Proceedings of the 34th annual Design Automation Conference
Multilevel hypergraph partitioning: applications in VLSI domain

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs

SIAM Journal on Scientific Computing
Multilevel algorithms for multi-constraint graph partitioning

SC '98 Proceedings of the 1998 ACM/IEEE conference on Supercomputing
Data Clustering Using Evidence Accumulation

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Combining Multiple Weak Clusterings

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Combining Multiple Clusterings Using Evidence Accumulation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Clustering: 50 Years Beyond K-means

ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
An Evolutionary Approach to Clustering Ensemble

ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 03
Clustering aggregation by probability accumulation

Pattern Recognition
A scalable framework for cluster ensembles

Pattern Recognition
Clustering Ensembles Using Ants Algorithm

IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part I: Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy
A binary method for fast computation of inter and intra cluster similarities for combining multiple clusterings

Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
On voting-based consensus of cluster ensembles

Pattern Recognition
Weighted partition consensus via kernels

Pattern Recognition

ASOD: Arbitrary shape object detection

Engineering Applications of Artificial Intelligence
Estimation of the number of clusters using heterogeneous multiple classifier system

ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Generation of a clustering ensemble based on a gravitational self-organising map

Neurocomputing
An efficient and scalable family of algorithms for combining clusterings

Engineering Applications of Artificial Intelligence
Ensemble canonical correlation analysis

Applied Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

Multiple clusterings are produced for various needs and reasons in both distributed and local environments. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. It is also expected that the final clustering is novel, robust, and scalable. In order to solve this challenging problem we introduce a new graph-based method. Our method uses the evidence accumulated in the previously obtained clusterings, and produces a very good quality final clustering. The number of clusters in the final clustering is obtained automatically; this is another important advantage of our technique. Experimental test results on real and synthetically generated data sets demonstrate the effectiveness of our new method.