The research interest of this paper is focused on the efficient clustering task for an arbitrary color data. In order to tackle this problem, we have tried to model the inherent uncertainty and vagueness of color data using fuzzy color model. By taking fuzzy approach to color modeling, we could make a soft decision for the vague regions between neighboring colors. The proposed fuzzy color model defined a three dimensional fuzzy color ball and color membership computation method with two inter-color distances. With the fuzzy color model, we developed a new fuzzy clustering algorithm for an efficient partition of color data. Each fuzzy cluster set has a cluster prototype which is represented by fuzzy color centroid.

翻译：本文的研究重点在于针对任意颜色数据的高效聚类任务。为解决该问题，我们尝试采用模糊颜色模型对颜色数据固有的不确定性与模糊性进行建模。通过模糊化颜色建模方法，我们能够对相邻颜色间的模糊区域进行软决策。所提出的模糊颜色模型定义了一个三维模糊颜色球体，以及基于两种颜色间距离的颜色隶属度计算方法。基于该模糊颜色模型，我们开发了一种新的模糊聚类算法，用于实现颜色数据的高效划分。每个模糊聚类集合均具有由模糊颜色质心表示的聚类原型。