Abstract: On the basis of Conditioned Fuzzy C-Means,it was proposed that a foreign condition is determined by a computational 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 indexes,FCM,Semantic Conditioned Fuzzy C-Means and its Spectral Optimization were tested on Iris data set．Experiment results show that the cluster that is closest to user semantic is able to be found by Semantic Conditioned Fuzzy C-Means.

Key words: Conditional clustering,Axiomatic fuzzy sets,Coherence membership function,Adjusted factor,Semantic strength expectation