1 research outputs found
Augmentation of the Reconstruction Performance of Fuzzy C-Means with an Optimized Fuzzification Factor Vector
Information granules have been considered to be the fundamental constructs of
Granular Computing (GrC). As a useful unsupervised learning technique, Fuzzy
C-Means (FCM) is one of the most frequently used methods to construct
information granules. The FCM-based granulation-degranulation mechanism plays a
pivotal role in GrC. In this paper, to enhance the quality of the degranulation
(reconstruction) process, we augment the FCM-based degranulation mechanism by
introducing a vector of fuzzification factors (fuzzification factor vector) and
setting up an adjustment mechanism to modify the prototypes and the partition
matrix. The design is regarded as an optimization problem, which is guided by a
reconstruction criterion. In the proposed scheme, the initial partition matrix
and prototypes are generated by the FCM. Then a fuzzification factor vector is
introduced to form an appropriate fuzzification factor for each cluster to
build up an adjustment scheme of modifying the prototypes and the partition
matrix. With the supervised learning mode of the granulation-degranulation
process, we construct a composite objective function of the fuzzification
factor vector, the prototypes and the partition matrix. Subsequently, the
particle swarm optimization (PSO) is employed to optimize the fuzzification
factor vector to refine the prototypes and develop the optimal partition
matrix. Finally, the reconstruction performance of the FCM algorithm is
enhanced. We offer a thorough analysis of the developed scheme. In particular,
we show that the classical FCM algorithm forms a special case of the proposed
scheme. Experiments completed for both synthetic and publicly available
datasets show that the proposed approach outperforms the generic data
reconstruction approach