In this paper, a hybrid model with both clustering mechanism and regression mechanism is built with aid of an allocation of information granularity. Some numeric prototypes constructed by clustering methods are expanded into granular prototypes. The regression module accepts granular prototypes and outcomes information granules. Some advantages of this idea can be concluded: 1). The constructed granular prototypes are best able to represent original data with comparable smaller quantity and high quality. 2). The granular output could be used to predict a new sample’s real number output by giving a rational range. 3). It can be used to solve clustering and regression problems simultaneously. Two different granularity allocation strategies are proposed and experimented while constructing granular prototypes: non-uniformly allocation of information granularity to each cluster and non-uniformly allocation to each feature. A comprehensive objective function is defined considering specificity and generality of the output. The allocation of granularity itself is in fact a multiple-parameters optimization problem which invokes the usage of an evolutionary method. Two popular methods (GA and PSO) are tried and compared with a real data set’s experiment. Three data sets are collected from UCI website to testify the effectiveness of our approach in the experimental part.

在本文中，借助信息粒度的分配，建立了具有聚类机制和回归机制的混合模型。一些通过聚类方法构造的数值原型被扩展为粒状原型。回归模块接受粒度原型和结果信息粒度。可以得出这个想法的一些优点：1）。构建的颗粒原型最能代表具有可比性的较小数量和高质量的原始数据。2）。粒度输出可用于通过给出合理范围来预测新样本的实数输出。3）。可用于解决聚类和回归问题同时地。在构建粒度原型时，提出并实验了两种不同的粒度分配策略：信息粒度非均匀分配到每个集群和非均匀分配到每个特征。考虑到输出的特殊性和一般性，定义了综合目标函数。粒度分配本身实际上是一个多参数优化问题，它调用了进化方法的使用。尝试了两种流行的方法（GA 和 PSO）并与真实数据集的实验进行了比较。从 UCI 网站收集了三个数据集，以证明我们的方法在实验部分的有效性。