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Information granules have been considered to be the fundamental constructs ofGranular Computing (GrC). As a useful unsupervised learning technique, FuzzyC-Means (FCM) is one of the most frequently used methods to constructinformation granules. The FCM-based granulation-degranulation mechanism plays apivotal role in GrC. In this paper, to enhance the quality of the degranulation(reconstruction) process, we augment the FCM-based degranulation mechanism byintroducing a vector of fuzzification factors (fuzzification factor vector) andsetting up an adjustment mechanism to modify the prototypes and the partitionmatrix. The design is regarded as an optimization problem, which is guided by areconstruction criterion. In the proposed scheme, the initial partition matrixand prototypes are generated by the FCM. Then a fuzzification factor vector isintroduced to form an appropriate fuzzification factor for each cluster tobuild up an adjustment scheme of modifying the prototypes and the partitionmatrix. With the supervised learning mode of the granulation-degranulationprocess, we construct a composite objective function of the fuzzificationfactor vector, the prototypes and the partition matrix. Subsequently, theparticle swarm optimization (PSO) is employed to optimize the fuzzificationfactor vector to refine the prototypes and develop the optimal partitionmatrix. Finally, the reconstruction performance of the FCM algorithm isenhanced. We offer a thorough analysis of the developed scheme. In particular,we show that the classical FCM algorithm forms a special case of the proposedscheme. Experiments completed for both synthetic and publicly availabledatasets show that the proposed approach outperforms the generic datareconstruction approach.

Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector