Semi-supervised model-based document clustering: a comparative study

DOI10.1007/s10994-006-6540-7zbMath1470.62101OpenAlexW2042857885MaRDI QIDQ851864

Shi Zhong

Publication date: 22 November 2006

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-006-6540-7

zbMATH Keywords

model-based clusteringdeterministic annealingconstrained clusteringsemi-supervised clusteringclustering with feedbackseeded clustering

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Content and Structure Coverage: Extracting a Diverse Information Subset ⋮ Clustering documents with labeled and unlabeled documents using fuzzy semi-Kmeans

Uses Software

• Bow
• WebACE
• OHSUMED

Cites Work

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• Text classification from labeled and unlabeled documents using EM
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• 10.1162/153244303321897735
• 10.1162/1532443041827943
• Multivariate Information Bottleneck
• Empirical and theoretical comparisons of selected criterion functions for document clustering
• An experimental comparison of model-based clustering methods
• Concept decompositions for large sparse text data using clustering

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