Learning from Cluster Examples

  title={Learning from Cluster Examples},
  author={Toshihiro Kamishima and Fumio Motoyoshi},
  journal={Machine Learning},
Learning from cluster examples (LCE) is a hybrid task combining features of two common grouping tasks: learning from examples and clustering. In LCE, each training example is a partition of objects. The task is then to learn from a training set, a rule for partitioning unseen object sets. A general method for learning such partitioning rules is useful in any situation where explicit algorithms for deriving partitions are hard to formalize, while individual examples of correct partitions are… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 22 references

Akth Nearest Neighbour Clustering Procedure

M. A. Wong, T. Lane
Journal of the Royal Statistical Society ( B ) • 1983
View 4 Excerpts
Highly Influenced

Objective Criteria for The Evaluation of Clustering Methods

W. M. Rand
J . of the American Statistical Association • 1971
View 3 Excerpts
Highly Influenced

Bayesian Classification (AutoClass): Theory and Results

Advances in Knowledge Discovery and Data Mining • 1996

Rule Formulation Based on

T. Japanese. Kamishima, M. Minoh, K. Ikeda
View 2 Excerpts

Rule formulation based on inductive learning for extraction and classification of diagram symbols

T. Kamishima, M. Minoh, K. Ikeda
Transactions of The Information Processing Society of Japan • 1995
View 1 Excerpt

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