In this work, one and two-dimensional lattices are studied theoretically by a statistical mechanical approach. The nearest and next-nearest neighbor interactions are both taken into account, and the approximate thermodynamic properties of the lattices are calculated. The results of our calculations show that: (1) even though the next-nearest neighbor interaction may have an insignificant ef...

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

The traditional statistical models assume the covariance matrices for two populations are identical and both populations need to be described by multivariate normal distribution. Clearly, these assumptions do not always reflect the real world. Rough set is a new technique for data mining domain application. In this study, establish a financial distress Classification model using Fuzzy Nearest–N...

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance KNN disadvantaged by presence outliers in small sample size datasets and its deteriorates with class imbalance. We propose local Bonferroni Mean Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) assigns label query dependent neare...

Higher Education, or tertiary education, is the final stage which optional in formal education. It usually organized form of a university, academy, seminary, high school, institute. Every institution needs qualified and professional educators because they have an important role process implementing Tri Dharma Education. Recruitment for teaching staff has several stages standardization assessmen...

In this paper, classification efficiency of the conventional K-nearest neighbor algorithm is enhanced by exploiting fuzzy-rough uncertainty. The simplicity and nonparametric characteristics of the conventional K-nearest neighbor algorithm remain intact in the proposed algorithm. Unlike the conventional one, the proposed algorithm does not need to know the optimal value of K. Moreover, the gener...

Clustering of objects is an important area of research and application in variety of fields. In this paper we present a good technique for data clustering and application of this Technique for data clustering in a closed area. We compare this method with K-nearest neighbor and K-means.  

Missing data handling is a challenging issue often dealt with in data mining and pattern classification. In this paper, a fuzzy c-means clustering algorithm based on pseudo-nearest-neighbor intervals for incomplete data is given. The data are first completed using the pseudo-nearest-neighbor intervals approach, then the data set can be clustered based on the fuzzy c-means algorithm for interval...

The emergence of wireless sensor network as has raised the need for cheap wireless indoor localization technique This paper considers the problem of fingerprinting indoor localization based on signal strength measurements RSS. A new approach based on Fuzzy logic has been put forward. The proposal makes use of k-nearest neighbor classification in signal space. The localization of target node is ...