  As an important technology in artificial intelligence Granular Computing
(GrC) has emerged as a new multi-disciplinary paradigm and received much
attention in recent years. Information granules forming an abstract and
efficient characterization of large volumes of numeric data have been
considered as the fundamental constructs of GrC. By generating prototypes and
partition matrix, fuzzy clustering is a commonly encountered way of information
granulation. Degranulation involves data reconstruction completed on a basis of
the granular representatives. Previous studies have shown that there is a
relationship between the reconstruction error and the performance of the
granulation process. Typically, the lower the degranulation error is, the
better performance of granulation is. However, the existing methods of
degranulation usually cannot restore the original numeric data, which is one of
the important reasons behind the occurrence of the reconstruction error. To
enhance the quality of degranulation, in this study, we develop an augmented
scheme through modifying the partition matrix. By proposing the augmented
scheme, we dwell on a novel collection of granulation-degranulation mechanisms.
In the constructed approach, the prototypes can be expressed as the product of
the dataset matrix and the partition matrix. Then, in the degranulation
process, the reconstructed numeric data can be decomposed into the product of
the partition matrix and the matrix of prototypes. Both the granulation and
degranulation are regarded as generalized rotation between the data subspace
and the prototype subspace with the partition matrix and the fuzzification
factor. By modifying the partition matrix, the new partition matrix is
constructed through a series of matrix operations. We offer a thorough analysis
of the developed scheme. The experimental results are in agreement with the
underlying conceptual framework
