クラスタ例からの学習

概要

論文の詳細を見る
In this paper, novel machine learning problem that handles clustering and a solution for this problem are described. The clustering is a method to divide a given set of individuals into clusters that are subsets having properties "internal cohesion" and "external isolation". The clustering is often used to get partition for a set of individuals desired to be fitted to some purpose. In such case, it is hard to define such desired partition by means of the properties internal cohesion and external isolation explicitly. But, it is usually easy to show desired partition itself. Therefore, by learning from examples that are pairs of a set of individuals and desired partition for the set, it is desirable to acquire a criterion that is used to get desired partition for any unknown set. In this paper, such a learning method is proposed. The method is different from ordinal "learning from examples". So we call it "Learning from Cluster Examples". In our prior work, the learning method is applied to a problem to divide a logic diagram image. The experimental results show that the proposed method learns more desired partitions than the ones in our prior work. This method is extended to be so general that is applied to logic diagram understanding but also dot pattern clustering or problems in other fields.
社団法人人工知能学会の論文
1997-03-01