Semi-supervised clustering ensemble based on multi-ant colonies algorithm

Authors:
Yan Yang;Hongjun Wang;Chao Lin;Jinyuan Zhang
Affiliations:
School of Information Science and Technology, Provincial Key Lab of Cloud Computing and Intelligent Technology, Southwest Jiaotong University, Chengdu, P.R. China;School of Information Science and Technology, Provincial Key Lab of Cloud Computing and Intelligent Technology, Southwest Jiaotong University, Chengdu, P.R. China;School of Information Science and Technology, Provincial Key Lab of Cloud Computing and Intelligent Technology, Southwest Jiaotong University, Chengdu, P.R. China;School of Information Science and Technology, Provincial Key Lab of Cloud Computing and Intelligent Technology, Southwest Jiaotong University, Chengdu, P.R. China
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

Citing 11
Cited 0

Diversity and adaptation in populations of clustering ants

SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Clustering with Instance-level Constraints

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions

The Journal of Machine Learning Research
Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques
Clustering Ensembles: Models of Consensus and Weak Partitions

IEEE Transactions on Pattern Analysis and Machine Intelligence
An aggregated clustering approach using multi-ant colonies algorithms

Pattern Recognition
Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised Clustering

ECML '07 Proceedings of the 18th European conference on Machine Learning
A consensus based approach to constrained clustering of software requirements

Proceedings of the 17th ACM conference on Information and knowledge management
On voting-based consensus of cluster ensembles

Pattern Recognition
Ensemble clustering in the belief functions framework

International Journal of Approximate Reasoning
Bagging-based spectral clustering ensemble selection

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Semi-supervised clustering ensemble has emerged as an important elaboration of classical clustering problem that improves quality and robustness in clustering by combining the results of different clustering components with user provided constraints. In this paper, we propose a novel semi-supervised consensus clustering algorithm based on multi-ant colonies. Our method incorporates pairwise constraints not only in each ant colony clustering process, but also in computing new similarity matrix during the multi-ant colonies ensemble. Experimental results demonstrate the effectiveness of the proposed method.