抄録
Linear fuzzy clustering is a local PCA technique, in which the Fuzzy c-Means (FCM)-like iterative procedure is performed by using linear varieties as the prototypes of clusters.
In Fuzzy c-Medoids (FCMdd), cluster prototypes are selected from data samples and clustering criteria are calculated by using only mutual distances among samples. Then, it can be easily applied to clustering of relational data.
This paper proposes an extended FCMdd approach for linear fuzzy clustering of relational data, which is useful for extracting plane-like sub-structures spanned by three representative objects (medoids).
In the algorithm, new prototype is given by solving a combinatorial optimization problem for searching medoids and the computational complexity is reduced by searching only from a subset of objects having large membership values.
The information summarization approach can be regarded as a multi-cluster-type multi-dimensional scaling for summarizing data into several 2-D planes.
