.png){kind=icon}
Return
Semi-supervised model-based document clustering: A comparative study
.png){kind=icon}
OA
AI
DOI：10.1007/s10994-006-6540-7.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Semi-supervised learning has become an attractive methodology for improving classification models and is often viewed as using unlabeled data to aid supervised learning. However, it can also be viewed as using labeled data to help clustering, namely, semi-supervised clustering. Viewing semi-supervised learning from a clustering angle is useful in practical situations when the set of labels available in labeled data are not complete, i.e., unlabeled data contain new classes that are not present in labeled data. This paper analyzes several multinomial model-based semi-supervised document clustering methods under a principled model-based clustering framework. The framework naturally leads to a deterministic annealing extension of existing semi-supervised clustering approaches. We compare three (slightly) different semi-supervised approaches for clustering documents: Seeded damnl, Constrained damnl, and Feedback-based damnl, where damnl stands for multinomial model-based deterministic annealing algorithm. The first two are extensions of the seeded k-means and constrained k-means algorithms studied by Basu et al. (2002); the last one is motivated by Cohn et al. (2003). Through empirical experiments on text datasets, we show that: (a) deterministic annealing can often significantly improve the performance of semi-supervised clustering; (b) the constrained approach is the best when available labels are complete whereas the feedback-based approach excels when available labels are incomplete.
Keywords:
semi-supervised clustering
seeded clustering
constrained clustering
clustering with feedback
model-based clustering
deterministic annealing
Journal
IF:
2.9
Papers:
2.7K
Citations:
3.4W
Organization
No organization information available
Cited Papers
ShareSaveFluorescent Nanostructures from Aromatic Diblock Copolymers via Atom Transfer Radical Polymerization
PREAI
ShareSaveShareSaveShareSavePersonalized Oncology Suite: integrating next-generation sequencing data and whole-slide bioimages
OAAI
OAAIShareSaveShareSaveMolecular relationships between Pseudomonas INC P-9 degradative plasmids TOL, NAH, and SAL.png)
Plasmid
PlasmidIF0
PREAI
ShareSave
View more