一种优化的语义条件模糊聚类

Optimized Semantic Conditioned Fuzzy C-Means

在条件模糊聚类的基础上,提出利用公理化模糊集的成员隶属度函数量化用户语义、确定外部条件的方法。引入调节因子新概念,以调节基于语义的成员隶属度和基于欧拉距离的模糊隶属度对聚类结果的影响,并最终建立了语义条件聚类和经典模糊聚类的统一框架。给出了语义聚类的评价指标——语义强度期望,以找到距离目标语义最近的聚类。为使条件模糊聚类的聚类准确性更高,对原始数据进行了谱变换,尔后进行语义条件聚类。利用Iris数据集,对标准模糊聚类、语义条件聚类和语义条件聚类的谱优化3个算法进行了多指标综合实验比较。实验结果表明,语义条件聚类能够发现最贴近用户给出的语义的聚类。

On the basis of Conditioned Fuzzy C-Means,it was proposed that a foreign condition is determined by a com-putational user semantic. The user semantic was computed by the membership function based Axiomatic Fuzzy Sets. Further, a new concept, Adjusted Factor, was introduced to adjust the impact of the membership based on semantic and that one based on Euclidean Distance on clustering results, and one uniformed coherence framework of Fuzzy C-Means and Conditioned Fuzzy C-Means was built up. In addition, Semantic Strength Expectation was brought forward in order to assess the clustering quality. Furthermore, in order to raise the clustering accuracy, Semantic Conditioned Fuzzy C-Means was processed after the raw data was transformed into spectral data. Finally, based on multiple assessment inde-xes, FCM, Semantic Conditioned Fuzzy C-Means and its Spectral Optimization were tested on Iris data set. Experiment results show that the duster that is closest to user semantic is able to be found by Semantic Conditioned Fuzzy C-Means.