Takagi–Sugeno Fuzzy Modeling Using Mixed Fuzzy Clustering

TLDR: The use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs) is proposed, which outperform FCM-based T–S FMs in four out of five datasets and k-nearest neighbors classifiers in five out ofFive datasets.

Abstract: This paper proposes the use of mixed fuzzy clustering (MFC) algorithm to derive Takagi–Sugeno (T–S) fuzzy models (FMs). Mixed fuzzy clustering handles both time invariant and multivariate time variant features, allowing the user to control the weight of each component in the clustering process. Two model designs based on MFC are investigated. In the first, the antecedent fuzzy sets of the T–S model are obtained from the clusters obtained by the MFC algorithm. In the second, FMs based on fuzzy c-means (FCM) are constructed over the input space of the partition matrix generated by MFC. The proposed fuzzy modeling approaches are used in health care classification problems, where time series of unequal lengths are very common. MFC-based T–S FMs outperform FCM-based T–S FMs in four out of five datasets and k -nearest neighbors classifiers in five out of five datasets. Dynamic time warping performs better than the Euclidean distance in one dataset and similarly in the remaining. Given the different nature of time variant and invariant data, the choice of a clustering algorithm that treats data differently should be considered for model construction.