Algorithms for clustering data
Algorithms for clustering data
Inferring decision trees using the minimum description length principle
Information and Computation
Boolean Feature Discovery in Empirical Learning
Machine Learning
Why progress in machine vision is so slow
Pattern Recognition Letters
A Learning Criterion for Stochastic Rules
Machine Learning - Computational learning theory
Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning
Inductive learning of characteristic concept descriptions from small sets of classified examples
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Partially supervised clustering for image segmentation
Pattern Recognition
Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
The Journal of Machine Learning Research
Constructive induction-based clustering method for ubiquitous computing environments
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Absolute and relative clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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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 easy to specify. In the past, clustering techniques have been applied to such problems, despite being essentially unsuited to the task. We present a technique that has qualitative advantages over standard clustering approaches. We demonstrate these advantages by applying our method to problems in two domains; one with dot patterns and one with more realistic vector-data images.