Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More speci_cally, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clustering...

This paper shows a summarization method. We focus on the construction of fuzzy prototypes by means of typical values. For this purpose, we use a formal framework of measures of comparison of values of attributes of data for the management of fuzzy databases especially for data mining. One of the originalities of this paper lies in the fact that values are fuzzy.

Challenged by real-world clustering problems this paper proposes a novel fuzzy clustering scheme of datasets produced in the context of intuitionistic fuzzy set theory. More specifically, we introduce a variant of the Fuzzy C-Means (FCM) clustering algorithm that copes with uncertainty and a similarity measure between intuitionistic fuzzy sets, which is appropriately integrated in the clusterin...

In testing hypotheses, we may confront with cases where data are recorded as non-precise (fuzzy) rather than crisp. In such situations, the classical methods of testing hypotheses are not capable and need to be generalized. In solving the problem of testing hypotheses based on fuzzy data, the fuzziness of the observed data leads to the fuzzy p-value. This paper has been focused to calculate fuz...

abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve acc...