Two phase semi-supervised clustering using background knowledge

Authors:
Kwangcheol Shin;Ajith Abraham
Affiliations:
School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 4
Cited 2

Constrained K-means Clustering with Background Knowledge

ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Clustering documents into a web directory for bootstrapping a supervised classification

Data & Knowledge Engineering - Special issue: WIDM 2003
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Pattern Recognition
Using clustering to learn distance functions for supervised similarity assessment

Engineering Applications of Artificial Intelligence

A Semi-supervised Clustering Algorithm Based on Must-Link Set

ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Semi-supervised clustering algorithm for haplotype assembly problem based on MEC model

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Using background knowledge in clustering, called semi-clustering, is one of the actively researched areas in data mining. In this paper, we illustrate how to use background knowledge related to a domain more efficiently. For a given data, the number of classes is investigated by using the must-link constraints before clustering and these must-link data are assigned to the corresponding classes. When the clustering algorithm is applied, we make use of the cannot-link constraints for assignment. The proposed clustering approach improves the result of COP k-means by about 10%.