• Honam Mathematical Journal
• /
• v.43 no.3
• /
• pp.417-431
• /
• 2021

In this study, we introduced the notions of rough statistical convergence and defined the set of rough statistical limit points of a sequence and obtained statistical convergence criteria associated with this set in 2-normed space. Then, we proved that this set is closed and convex in 2-normed space. Also, we examined the relations between the set of statistical cluster points and the set of rough statistical limit points of a sequence in 2-normed space.

• Honam Mathematical Journal
• /
• v.43 no.1
• /
• pp.47-58
• /
• 2021

In this paper, we introduce rough statistical convergence of difference double sequences in normed linear spaces as an extension of rough convergence. We define the set of rough statistical limit points of a difference double sequence and analyze the results with proofs.

• Korean Journal of Mathematics
• /
• v.32 no.1
• /
• pp.121-135
• /
• 2024

In this paper, we have presented rough ideal statistical convergence of sequence on neutrosophic normed spaces as a significant convergence criterion. As neutrosophication can handle partially dependent components, partially independent components and even independent components involved in real-world problems. By examining some properties related to rough ideal convergence in these spaces we have established some equivalent conditions on the set of ideal statistical limit points for rough ideal statistically convergent sequences.

• Honam Mathematical Journal
• /
• v.46 no.3
• /
• pp.428-451
• /
• 2024

Within the neutrosophic normed space (𝔑𝔑𝔖), we present the notion of rough lacunary statistical convergence of double sequences in this study. Additionally, we delve into the exploration of rough lacunary statistical cluster points for double sequences in 𝔑𝔑𝔖 and scrutinize the correlation between this set of cluster points and the set of rough lacunary statistical limit points associated with the mentioned convergence.

• Journal of applied mathematics & informatics
• /
• v.40 no.3_4
• /
• pp.619-632
• /
• 2022

In this study, we examine rough ${\Delta}\mathcal{I}$-statistical convergence for difference sequences as an extension of rough convergence. We investigate the set of rough ${\Delta}\mathcal{I}$-statistical limit points of a difference sequence and analyze the results with proofs.

• Journal of applied mathematics & informatics
• /
• v.37 no.5_6
• /
• pp.459-467
• /
• 2019

In this work, via Orlicz functions, we have obtained a generalization of rough statistical convergence of asymptotically equivalent triple sequences a new non-matrix convergence method, which is intermediate between the ordinary convergence and the rough statistical convergence. We also have examined some inclusion relations related to this concept. We obtain the results are non negative real numbers with respect to the partial order on the set of real numbers.

• Annals of Fuzzy Mathematics and Informatics
• /
• v.16 no.3
• /
• pp.337-361
• /
• 2018

We introduce and study some basic properties of rough $I_{\lambda}$-statistical convergent of weight g (A), where $g:{\mathbb{N}}^3{\rightarrow}[0,\;{\infty})$ is a function statisying $g(m,\;n,\;k){\rightarrow}{\infty}$ and $g(m,\;n,\;k){\not{\rightarrow}}0$ as $m,\;n,\;k{\rightarrow}{\infty}$ and A represent the RH-regular matrix and also prove the Korovkin approximation theorem by using the notion of weighted A-statistical convergence of weight g (A) limits of a triple sequence of Bernstein-Stancu polynomials.

• Journal of information and communication convergence engineering
• /
• v.9 no.1
• /
• pp.55-58
• /
• 2011

In this paper we introduce an improved method to measure saliency values of pixels from an image. The proposed saliency measure is formulated using local features of color and a statistical framework. In the preprocessing step, rough salient pixels are determined as the local contrast of an image region with respect to its neighborhood at various scales. Then, the saliency value of each pixel is calculated by Bayes' rule using rough salient pixels. The experiments show that our approach outperforms the current Bayes' rule based method.

• Journal of Digital Convergence
• /
• v.12 no.8
• /
• pp.257-264
• /
• 2014

This paper presents the core competencies diagnosis system which targeted our collegiate students in an attempt to induce the core competencies for reinforcing the learning and employment capabilities. Because these days data give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. So as to address the measurement of uncertainties from the classification of categorical data and the implementation of its analytic system, an uncertainty measure of rough entropy and information entropy is defined so that similar behaviors analysis is carried out and the clustering ability is demonstrated in the comparison with the statistical approach. Because the acquired and necessary competencies of the collegiate is deduced by way of the results of the diagnosis, i.e. common core competencies and major core competencies, they facilitate not only the collegiate life and the employment capability reinforcement but also the revitalization of employment and the adjustment to college life.

• Journal of Digital Convergence
• /
• v.11 no.10
• /
• pp.387-394
• /
• 2013

Data mining is the process of discovering hidden, non-trivial patterns in large amounts of data records in order to be used very effectively for analysis and forecasting. Because hundreds of variables give rise to a high level of redundancy and dimensionality with time complexity, they are more likely to have spurious relationships, and even the weakest relationships will be highly significant by any statistical test. Hence cluster analysis is a main task of data mining and is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. In this paper system implementation is of great significance, which defines a new definition based on information-theoretic entropy and analyse the analogue behaviors of objects at hand so as to address the measurement of uncertainties in the classification of categorical data. The sources were taken from a survey aimed to identify of job guidance from students in high school pyeongtaek. we show how variable precision information-entropy based rough set can be used to group student in each section. It is proved that the proposed method has the more exact classification than the conventional in attributes more than 10 and that is more effective in job guidance for students.