Enforcement of rough fuzzy clustering based on correlation analysis

Abstract EN

Clustering is a standard approach in analysis of data and construction of separated similar groups.

The most widely used robust soft clustering methods are fuzzy, rough and rough fuzzy clustering.

The prominent feature of soft clustering leads to combine the rough and fuzzy sets.

The Rough Fuzzy C-Means (RFCM) includes the lower and boundary estimation of rough sets, and fuzzy membership of fuzzy sets into c-means algorithm, the widespread RFCM needs more computation.

To avoid this, this paper proposes Fuzzy to Rough Fuzzy Link Element (FRFLE) which is used as an important factor to conceptualize the rough fuzzy clustering from the fuzzy clustering result.

Experiments with synthetic, standard and the different benchmark dataset shows the automation process of the FRFLE value, then the comparison between the results of general RFCM and RFCM using FRFLE is observed.

Moreover, the performance analysis result shows that proposed RFCM algorithm using FRFLE deals with less computation time than the traditional RFCM algorithms.