<p>Aiming at the defects of fuzzy clustering based on Ruspini partitioning in revealing the relationship between categories, a new generalized harmonic fuzzy partition C-means clustering algorithm is proposed in this paper. Firstly, based on the existing concept of Zadeh’s fuzzy set, the new concept of generalized harmonic fuzzy set is introduced and some basic operations of generalized harmonic fuzzy sets are given. Secondly, the axiomatic definitions and specific expressions of fuzzy entropy and similarity for generalized harmonic sets applied to pattern analysis and machine intelligence are provided. Again, the corresponding harmonic fuzzy partition for cluster analysis are further defined. Fourthly, based on the concept of harmonic fuzzy partition, we propose a novel generalized harmonic fuzzy partition C-means clustering algorithm. Finally, the competitiveness and advantages of the proposed algorithm are verified by comparing with existing representative fuzzy clustering algorithms.</p>