Abstract
Learning from cluster examples (LCE) is a composite task of two common classification tasks: learning from examples and clustering. Learning from cluster examples involves an attempt to acquire a rule that can be used to partition an unseen object set from given examples. 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 this paper, to improve estimation accuracy of LCE, we employ attributes of clusters and propose a method that can handle this type of attributes. We show improvements of performance by applying this method to artificial data.
