一种建立粗糙数据模型的监督模糊聚类方法

An Approach to Building Rough Data Model Through Supervised Fuzzy Clustering

提出了在输入-输出积空间中利用监督模糊聚类技术快速建立粗糙数据模型(rough data model,简称RDM)的一种方法.该方法将RDM模型的分类质量性能指标与具有良好特性的Gustafson-Kessel(G-K)聚类算法结合在一起,并通过引入数据对模糊类的推定隶属度的概念,给出了将模糊聚类模型转化为粗糙数据模型的方法,从而设计出一种通过迭代计算使目标函数最小的两个必要条件方程来获取RDM模型的有效算法,将Kowalczyk方法的多维搜索过程变为以聚类数目为参数的一维搜索,极大地减少了寻优时间.与传统的粗糙集理论和Kowalczyk方法相比,提出的方法具有更好的数据概括能力和噪声数据处理能力.最后,通过不同的数据集实验测试,结果表明了该方法的有效性.

A new method for fast building the rough data model (RDM) by means of supervised fuzzy clustering in the product space of input and output variables is proposed. The approach incorporates the RDM’s classification quality performance index with Gustafson-Kessel (GK) clustering algorithm and is of many good properties. The way to convert the fuzzy cluster models to rough data models by introducing the concept of putative membership degree of a data point to a fuzzy cluster is suggested. Hence, an efficient algorithm that can obtain RDMs by just iteratively computing two necessary condition equations is worked out. It minimizes the objective function and turns the multi-dimensional search process of the Kowalczyk’s method to one dimensional search strategy (in terms of the number of clusters). This technique reduces the searching time greatly. Compared with the traditional rough set theory and the Kowalczyk’s method, the approach has more powerful ability to handle data contaminated by noise and better generalization ability. Finally, different examples of data sets illustrate the effectiveness of the approach.