Enhanced Oil Recovery (EOR) is a well-known method to increase oil production from oil reservoirs. Applying EOR to a new reservoir is a costly and time consuming process. Incorporating available knowledge of oil reservoirs in the EOR process eliminates these costs and saves operational time and work. This work presents a universal method to apply EOR to reservoirs based on the available data by clustering the data into compact and well-separated groups. A label is then assigned to each cluster which is in fact class of the data points belonging to that cluster. When EOR is intended to be applied to a new reservoir, class of the reservoir is determined and then EOR method used for the reservoirs of that class is applied to this one with no further field studies and operations. In contrast to classification, clustering is unsupervised and number of clusters within the data is not known a priori. Some well-known methods for determining number of clusters are tried but they failed. A novel method is presented in this work for number of clusters based on difference of membership grades of the data points in the clusters. It is applied to both synthetic and real life data including reservoirs data and it is shown that this method finds number of clusters accurately. It is also shown the raw data could be easily represented as fuzzy rule-base for better understanding and interpretation of the data.

Nowadays, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company’s customers. But, CLV calculation encounters some difficulties which limit the usage of this technique. Nonetheless, companies looking for methods to know how to calculate their customers’ CLV. In this paper, fuzzy classification rules were used to determine customers’ CLV and segment them based on recency, frequency and monetary (RFM) measures. Data required for applying this method gathered from a steel firm in Iran.

This paper considers the generation of some interpretable fuzzy rules for assigning an amino acid sequence into the appropriate protein super families. Since the main objective of this classifier is the interpretability of rules, we have used the distribution of amino acids in the sequences of proteins as features. These features are the occurrence probabilities of six exchange groups in the sequences. To generate the fuzzy rules, we have used some modified versions of a common approach. The generated rules are simple and understandable, especially for biologists. To evaluate our fuzzy classifiers, we have used four protein super families from UniProt database. Experimental results show the comprehensibility of generated fuzzy rules with comparable classification accuracy.

This paper presents a rock mass classification system development based on the theory of fuzzy sets. This classification system, which is a generalization of the conventional method of rock mass rating, allows us to introduce uncertainties as well as evaluating overall reliability. Appropriate fuzzy sets are assigned to die rock mass parameters and the procedure required to find the final fuzzy-based rock mass rating (FRMR) is discussed in detail. The proposed classification system provides the rock mass rating, FRMR, together with its reliability, in addition to the corresponding rock mass class aid estimates of cohesion and friction angle. Comparison of FRMR with die conventional rock mass rating, RMR, indicates that different combinations of rock mass parameters leading to the same RMR can have different FRMR values. However, on average there is very good agreement between the two classification systems.

The fuzzy K-Nearest Neighbour (FKNN) method is a classification approach that integrates fuzzy theories with the K-Nearest Neighbour classifier. The algorithm computes the degree of membership for a given dataset within each class and then chooses the class with the highest degree of membership as the assigned classification outcome. This algorithm has several applications in regression problems. When the mathematical model of the data is not known, this method can be used to estimate and approximate the value of the response variable. This paper introduces a method, which incorporates a parameter distance measure to empower decision makers to make precise selections across several levels. Furthermore, we provide an analysis of the algorithm's strengths and shortcomings, as well as a comprehensive explanation of the distinctions between the closest neighbour approach in tasks of classification and regression. Finally, to further elucidate the principles, we present a range of examples that demonstrate the application of closest neighbour algorithms in the classification and regression of fuzzy numbers.