Magnifying Elements in Semigroups of Fixed Point Set Transformations Restricted by an Equivalence Relation

Let X be a nonempty set and ρ be an equivalence relation on X . For a nonempty subset S of X , we denote the semigroup of transformations restricted by an equivalence relation ρ fixing S pointwise by E F S X , ρ . In this paper, magnifying elements in E F S X , ρ will be investigated. Moreover, we will give the necessary and sufficient conditions for elements in E F S X , ρ to be right or left magnifying elements.

Neural network-based models of binomial time series in data analysis problems

This article is devoted to constructing neural network-based models for discrete-valued time series and their use in computer data analysis. A new family of binomial time series based on neural networks is presented, which makes it possible to approximate the arbitrary-type stochastic dependence in time series. Ergodicity conditions and an equivalence relation for these models are determined. Consistent statistical estimators for model parameters and algorithms for computer data analysis (including forecasting and pattern recognition) are developed.

Reduced finitary incidence algebras and their automorphisms

Let [Formula: see text] denote the incidence algebra of a locally finite poset [Formula: see text] over a field [Formula: see text] and [Formula: see text] some equivalence relation on the set of generators of [Formula: see text]. Then [Formula: see text] is the subset of [Formula: see text] of all the elements that are constant on the equivalence classes of [Formula: see text]. If [Formula: see text] satisfies certain conditions, then [Formula: see text] is a subalgebra of [Formula: see text] called a reduced incidence algebra. We extend this notion to finitary incidence algebras [Formula: see text] for any poset [Formula: see text]. We investigate reduced finitary incidence algebras [Formula: see text] and determine their automorphisms in some special cases.

Global aspects of measure preserving equivalence relations and graphs

This paper is an introduction and survey of a “global” theory of measure preserving equivalence relations and graphs. In this theory one views a measure preserving equivalence relation or graph as a point in an appropriate topological space and then studies the properties of this space from a topological, descriptive set theoretic and dynamical point of view.

Properties of nano Δ* open sets

This article aims at introducing nano Δ* open sets in Nano Topological Spaces (NTS). The NTS are nothing but the spaces created in terms of equivalence relation which is derived from lower approximation, upper approximation and boundaries of a subset of a universal set. An elaborate study on various properties and characterizations of Nano Δ* open sets in relation with nano δ open sets and nano δg-kemel operator are explained in this article.

ON SOME EQUIVALENCE RELATION ON NON-ABELIAN $\CA$-GROUPS

A non-abelian group $G$ is called a $\CA$-group ($\CC$-group) if $C_G(x)$ is abelian(cyclic) for all $x\in G\setminus Z(G)$. We say $x\sim y$ if and only if $C_G(x)=C_G(y)$.We denote the equivalence class including $x$ by$[x]_{\sim}$. In this paper, we prove thatif $G$ is a $\CA$-group and $[x]_{\sim}=xZ(G)$, for all $x\in G$, then $2^{r-1}\leq|G'|\leq 2^{r\choose 2}$.where $\frac {|G|}{|Z(G)|}=2^{r}, 2\leq r$ and characterize all groups whose $[x]_{\sim}=xZ(G)$for all $x\in G$ and $|G|\leq 100$. Also, we will show that if $G$ is a $\CC$-group and $[x]_{\sim}=xZ(G)$,for all $x \in G$, then $G\cong C_m\times Q_8$ where $C_m$ is a cyclic group of odd order $m$ andif $G$ is a $\CC$-group and $[x]_{\sim}=x^G$, for all $x\in G\setminus Z(G)$, then $G\cong Q_8$.

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PDF