In this paper, the utility of credibilistic critical values in crisp conversion of fuzzy data sets is considered. Conversion of this type becomes essential mainly when clustering of fuzzy data sets is carried out. In this paper performance of two popular clustering algorithms namely Fuzzy c–means and Fuzzy c–medoids algorithms are evaluated under credibilistic critical value crisp conversion is...

Fuzzy K-means clustering algorithm is a popular approach for exploring the structure of a set of patterns, especially when the clusters are overlapping or fuzzy. However, the fuzzy K-means clustering algorithm cannot be applied when the real-life data contain missing values. In many cases, the number of patterns with missing values is so large that if these patterns are removed, then sufficient...

We describe the basics of fuzzy sets and fuzzy logic. Based upon the concept of linguistic values, which describe imprecise concepts using words, the basics of fuzzy rules and fuzzy inference are introduced. In the second part we briefly explain applications of fuzzy rules for function approximation using fuzzy graphs, clustering using fuzzy algorithms, and classification under uncertainty usin...

In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classi cation The presented method is mathematically solid but never theless easy and e cient to implement Three possible applications of this methodology are outlined the classi cation of patterns with an incomplete feature vector the com pletion of the input vector when a certain class is des...

The optimal hypothesis tests for the binomial distribution and some other discrete distributions are uniformly most powerful (UMP) one-tailed and UMP unbiased (UMPU) two-tailed randomized tests. Conventional confidence intervals are not dual to randomized tests and perform badly on discrete data at small and moderate sample sizes. We introduce a new confidence interval notion, called fuzzy conf...

We consider the problem of testing a statistical hypothesis where the scientifically meaningful test statistic is a function of latent variables. In particular , we consider detection of genetic linkage, where the latent variables are patterns of inheritance at specific genome locations. Fuzzy p-values, introduced by Geyer & Meeden (2005) are random variables (described by their probability dis...

Fuzzy logic can be used to analyse and classify flora faunal diversity. It uses fuzzy describe richness complexity of plant animal life. identify patterns in data better understand diversity an ecosystem. This study a combined effect time quantity dependent matrix predict the distribution phytal fauna Erayamanthurai coast. The method involved use Initial Raw Data Matrix (IRDM), Average Quantity...

In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classiication. The presented method is mathematically solid but nevertheless easy and eecient to implement. Three possible applications of this methodology are outlined: the classiication of patterns with an incomplete feature vector, the completion of the input vector when a certain class is d...

This paper extends a comparison measure called the statisfaction function(SF). The SF estimates the degree to which arithmetic comparisons between two fuzzy values are satissed. The previously proposed SF was deened on a discrete domain(SFD). So, in order to compare continuous fuzzy values, the fuzzy values should be converted into discrete ones. This paper deenes a satisfaction function on a c...

In order to obtain the information from an L-Fuzzy context, the complete relation between the objects and the attributes is needed. However, the contexts that model many situations have absent values. To solve this problem, at the beginning of the paper we remind the interval-valued linguistic variable definition and, later, we propose an extension of the fuzzy propositions to the interval-valu...