8,032 research outputs found
Automatic generation of fuzzy classification rules using granulation-based adaptive clustering
A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used
The Ideal Candidate. Analysis of Professional Competences through Text Mining of Job Offers
The aim of this paper is to propose analytical tools for identifying peculiar aspects of job market for graduates. We propose a strategy for dealing with daa tat have different source and nature
Evaluating strategies for implementing industry 4.0: a hybrid expert oriented approach of B.W.M. and interval valued intuitionistic fuzzy T.O.D.I.M.
open access articleDeveloping and accepting industry 4.0 influences the industry structure and customer willingness. To a successful transition to industry 4.0, implementation strategies should be selected with a systematic and comprehensive view to responding to the changes flexibly. This research aims to identify and prioritise the strategies for implementing industry 4.0. For this purpose, at first, evaluation attributes of strategies and also strategies to put industry 4.0 in practice are recognised. Then, the attributes are weighted to the expertsâ opinion by using the Best Worst Method (BWM). Subsequently, the strategies for implementing industry 4.0 in Fara-Sanat Company, as a case study, have been ranked based on the Interval Valued Intuitionistic Fuzzy (IVIF) of the TODIM method. The results indicated that the attributes of âTechnologyâ, âQualityâ, and âOperationâ have respectively the highest importance. Furthermore, the strategies for ânew business models developmentâ, âImproving information systemsâ and âHuman resource managementâ received a higher rank. Eventually, some research and executive recommendations are provided. Having strategies for implementing industry 4.0 is a very important solution. Accordingly, multi-criteria decision-making (MCDM) methods are a useful tool for adopting and selecting appropriate strategies. In this research, a novel and hybrid combination of BWM-TODIM is presented under IVIF information
Fuzzy C-ordered medoids clustering of interval-valued data
Fuzzy clustering for interval-valued data helps us to find natural vague boundaries in such data. The
Fuzzy c-Medoids Clustering (FcMdC) method is one of the most popular clustering methods based on a
partitioning around medoids approach. However, one of the greatest disadvantages of this method is its
sensitivity to the presence of outliers in data. This paper introduces a new robust fuzzy clustering
method named Fuzzy c-Ordered-Medoids clustering for interval-valued data (FcOMdC-ID). The Huber's
M-estimators and the Yager's Ordered Weighted Averaging (OWA) operators are used in the method
proposed to make it robust to outliers. The described algorithm is compared with the fuzzy c-medoids
method in the experiments performed on synthetic data with different types of outliers. A real application of the FcOMdC-ID is also provided
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