The traditional K-means clustering algorithm is difficult to initialize the number of clusters K, and the initial cluster centers are selected randomly, this makes the clustering results very unstable. Meanwhile, algorithms are susceptible to noise points. To solve the problems, the traditional K-means algorithm is improved. The improved method is divided into the same grid in space, according ...

Steganography is the art and science of embedding secret data in another medium to prevent the leakage of secret information. A VQ-based (vector quantization) steganographic method usually involves changes of the block values in the VQ images, which might cause serious distortion. As a result, many existing methods use closest pairs or clustering techniques to preserve an acceptable image quali...

This paper proposes rough convergence and statistical of a double sequence in intuitionistic fuzzy normed spaces. It then defines the limit points cluster these Afterwards, this examines some their basic properties. Finally, it discusses need for further research.

The purpose of this paper is to introduce the concepts of lacunary almost statistical limit and cluster points of sequences of fuzzy numbers. For any lacunary sequence = ( ); and for any sequence = ( ) of fuzzy numbers, we introduce new sets: Λ ( ), Γ ( ),Λ ̂ ( ),Γ ( ) and obtain some relations between these sets.

In the present paper, we prove a characterization theorem which gives a necessary and sufficient condition for a sequence of fuzzy numbers to be levelwise statistically convergent in the space of fuzzy numbers. As an application of this theorem we utilize the idea of statistical equi-continuity in order to obtain a condition which guarantees the set of levelwise statistical cluster points of a ...

This article uses cluster analysis to develop an early warning model of political change in the Levant as reflected in WEIS-coded event data generated from Reuters between April 1979 and December 1998. We employ a new statistical algorithm that uses the correlation between dyadic behaviors at two time points in time to identify clusters of political activity. The transition to a new cluster occ...

Nearest neighbor (k-NN) graphs are widely used in machine learning and data mining applications, and our aim is to better understand what they reveal about the cluster structure of the unknown underlying distribution of points. Moreover, is it possible to identify spurious structures that might arise due to sampling variability? Our first contribution is a statistical analysis that reveals how ...

The expectation and the mean of partitions generated by a cluster ensemble are not unique in general. This issue poses challenges in statistical inference and cluster stability. In this contribution, we state sufficient conditions for uniqueness of expectation and mean. The proposed conditions show that a unique mean is neither exceptional nor generic. To cope with this issue, we introduce homo...