Guided Cluster Discovery with Markov Model

Authors:
C. H. Li
Affiliations:
Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong. chli@comp.hkbu.edu.hk
Venue:
Applied Intelligence
Year:
2005

Citing 4
Cited 2

Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
Chameleon: Hierarchical Clustering Using Dynamic Modeling

Computer
Enhancing Supervised Learning with Unlabeled Data

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On clusterings-good, bad and spectral

FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science

Incremental development of CBR strategies for computing project cost probabilities

Advanced Engineering Informatics
Semi-supervised Bayesian ARTMAP

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cluster discovery is an essential part of many data mining applications. While cluster discovery process is mainly unsupervised in nature, it can often be aided by a small amount of labeled data. A probabilistic model on the clustering structure is adopted and a novel unified energy equation for clustering that incorporates both labeled data and unlabeled data is introduced. This formulation is inspired by a force-field model integrating labeling constraint on labeled data and similarity information on unlabeled data for joint estimation. Experimental results show that good clusters can be identified using small amount of labeled data.