Abstract
Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.
Highlights
Shukla and Muhuri (2019) [50] proposed IT2 implementation to cluster big data in modeling gene expression. They generated FT1 set may of uncertainty (FOU) for the IT2 implementation with the aim to account for all possible uncertainties that occurred in the big dataset of gene expression
To ensure that our review covers the important components of fuzzy logic, namely its calculation and operation, we extended the manuscript gathering by searching using the keywords “fuzzy calculation” and “fuzzy operation”
Fuzzy C-Means (FCM) is used to construct the MF for fuzzy inference system (FIS) due to its superior capability over the expert knowledge-based approach in terms of time consumption and subjectivity in making decisions
Summary
The innate nature of information, by default, is tied to the concept of uncertainty The factor for this uncertainty is information insufficiency which may be in the form of incomplete, unreliable, ambiguous, fragmented, or a combination of these forms [1,2]. To relate this construction of IT2 MF with FCM, the original FT1 MF in Figure 3a can be constructed using the outputs of FCM and a heuristic method. This approach only produces Gaussian FT1 MF, imposing a blurring method on it will generate
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