A novel fuzzy clustering algorithm based on rough set and inhibitive factor

As an important data analysis method, rough set theory can be used to analyze data with uncertainties. Rough set is able to acquire knowledge by the indistinguishable relationship among data objects without any prior knowledge. Rough set theory provides a new theoretical means for solving soft computing problems and has a wide application space in data mining. Meanwhile, the fuzzy C-means clustering algorithm is sensitive to noisy points and has low convergence speed. In order to deal with the above problems, a novel fuzzy clustering algorithm based on rough set and inhibitive factor is proposed. According to the related concepts of rough set theory, the membership model of fuzzy C-means algorithm is redefined. Besides, an inhibitive factor is set to improve the convergence speed of the algorithm under the premise of guaranteeing the clustering effect.

Automated summary

Rough set theory is an important method for analyzing data with uncertainties, able to acquire knowledge by the indistinguishable relationship among data objects without any prior knowledge. The novel fuzzy clustering algorithm based on rough set and inhibitive factor shows potential application value in data mining by improving the convergence speed while guaranteeing the clustering effect.