Guided cluster discovery with Markov model

C.H. Li

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)37-46
Number of pages10
JournalApplied Intelligence
Issue number1
Publication statusPublished - Jan 2005
Externally publishedYes

Scopus Subject Areas

  • Artificial Intelligence

User-Defined Keywords

  • Clustering semi-supervised learning
  • Markov model


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