Fuzzy Style K-Plane Clustering

As the first attempt, this article considers how to provide a design methodology for style clustering on stylistic data, where each cluster depends on both the similarities between data samples and its latently or apparently distinguishable style. By taking our previous fuzzy k plane clustering algorithm as the basic framework, a fuzzy style k-plane clustering (S-KPC) algorithm is proposed to have its distinctive merits: First, the nuances between styles of clusters can be well identified by using the proposed twofold data representation. That is to say, style matrices are used to express the structure, hence style information of each cluster, whereas the augmentation of the original features of data with enhanced nodes is taken as an abstract representation so as to move the manifold structure of data apart. Such a twofold data representation can make us realize S-KPC readily in an incremental way. Second, by means of alternating optimization strategy, the objective function of S-KPC can be optimized such that each discriminant function of each cluster shares the advantages of both simple regression models and functional-link neural networks. Extensive experiments on synthetic and real-world datasets demonstrate that S-KPC has comparable clustering performance with several compared methods on the adopted ordinary datasets, and yet it obviously outperforms them on stylistic datasets.

Automated summary

This article introduces a new clustering algorithm for stylistic data, which aims to differentiate subtle differences between clusters of different styles. The objective function of the algorithm is optimized through alternating optimization strategy.