Clustering is a key process in data mining for revealing structure and patterns in data. Fuzzy C-means (FCM) is a popular algorithm using a partitioning approach for clustering. One advantage of FCM is that it converges rapidly. In addition, using fuzzy sets to represent the degrees of cluster membership of each data point provides more information regarding relationships within the data than d...

When designing rule-based models and classifiers, some precision is sacrificed to obtain linguistic interpretability. Understandable models are not expected to outperform black boxes, but usually fuzzy learning algorithms are statistically validated by contrasting them with black-box models. Unless performance of both approaches is equivalent, it is difficult to judge whether the fuzzy one is d...

The term approximate reasoning (AR) refers to methods and methodologies that enable reasoning with imprecise inputs to obtain meaningful outputs. AR schemes involving fuzzy sets are one of the best known applications of fuzzy logic in the wider sense. Fuzzy Inference Systems (FIS) have many degrees of freedom, viz., the underlying fuzzy partition of the input and output spaces, the fuzzy logic ...

For complex and high-dimensional problems, data-driven identification of classifiers has to deal with structural issues like the selection of the relevant features and effective initial partition of the input domain. Therefore, the identification of fuzzy classifiers is a challenging topic. Decision-tree (DT) generation algorithms are effective in feature selection and extraction of crisp class...

In this paper, a clustering-based method is proposed for automatically constructing a multi-input TakagiSugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to prov...

In this paper we present a study on inference in a Fuzzy Logic Controller (FLC). Many different implication operators have been proposed in the Fuzzy Logic literature; we take into account five of them that have already been used in fuzzy control. In order to evaluate the advantages and disadvantages of each implication in practical applications, we consider the controller's defuzzified output ...

Many algorithms for fuzzy clustering depend on initial guesses of cluster prototypes, and on assumptions made as to the number of subgroups present in the data. This study reports on a method for carrying out fuzzy classification without apriori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolnme and density cr...

In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy...

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of informatio...

This paper proposes to cluster any finite data set by optimizing with respect to a cluster score function, assigning a positive real worth to each data subset and thereby dealt with in pseudo-Boolean form, so that the multilinear extension (MLE) allows to evaluate fuzzy data subsets or clusters as well. A fuzzy clustering being a collection of fuzzy clusters over which every data point has to d...