Learning from Cluster Examples Export

@article{kamishima2003learning, abstract = {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.}, author = {Kamishima, Toshihiro and Motoyoshi, Fumio}, citeulike-article-id = {6061237}, citeulike-linkout-0 = {http://dx.doi.org/10.1023/A:1026351106797}, citeulike-linkout-1 = {http://www.springerlink.com/content/l731306r030n8g3r}, day = {1}, doi = {10.1023/A:1026351106797}, journal = {Machine Learning}, keywords = {supervised-clustering}, month = {December}, number = {3}, pages = {199--233}, posted-at = {2009-11-03 17:59:23}, priority = {2}, title = {Learning from Cluster Examples}, url = {http://dx.doi.org/10.1023/A:1026351106797}, volume = {53}, year = {2003} }

TY - JOUR ID - kamishima2003learning L3 - citeulike-article-id:6061237 N2 - 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. IS - 3 JF - Machine Learning EP - 233 TI - Learning from Cluster Examples VL - 53 SP - 199 KW - supervised-clustering AU - Kamishima, Toshihiro AU - Motoyoshi, Fumio PY - 2003/12/01/ UR - http://dx.doi.org/10.1023/A:1026351106797 ER -