Visualization Metaphors for Fuzzy Cognitive Maps

Podvesovskii, Aleksandr; Isaev, Ruslan

doi:10.26583/sv.10.4.02

Cited by 19 publications

(20 citation statements)

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“…In [3], it was noted that many of cognitive clarity criteria contradict each other, and it is impossible in the general case to ensure that FCM visual image meets all the criteria at the same time from an algorithmic point of view. However, a number of features of FCM visualization process are noteworthy: 1) existing graph visualization algorithms [5,6] ensure the formation of sufficiently acceptable (from the point of view of individual cognitive clarity criteria) visual images of an FCM; 2) building FCM visual image using such algorithms does not require large computational and time resources.…”

Section: Development Of Fcm Visualization Metaphor: Basic Conceptmentioning

confidence: 99%

“…9) by 4 times. The resulting visual image can be adapted for visual analysis at any stage of cognitive modeling by using the appropriate representation metaphor (this issue was discussed in detail in the authors' paper [3]). So, the use of the metaphor shown in Fig.…”

Section: Fig 9 Fcm Visual Imagementioning

confidence: 99%

“…One of the conditions for effective work with a cognitive model is ensuring its visual representation. In [3], the authors proposed an approach to FCM visualization based on using the concept of visualization metaphor and its two components -spatial metaphor and representation metaphor [4]. FCM visualization metaphor is based on graph visualization algorithms [5,6] and cognitive clarity concept, which characterizes the ease of intuitive understanding of information [7] and takes into account the problem of human's limited cognitive abilities when reading graphs (a detailed analysis of this problem can be found, for example, in [8]).…”

Section: Introductionmentioning

confidence: 99%

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Constructing Optimal Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

Podvesovskii¹,

Isaev²

2019

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The paper presents continuation of research in the field of constructing a visualization metaphor of cognitive models based on fuzzy cognitive maps. Development of this metaphor in the direction of automating the construction of a visual image of a fuzzy cognitive map, which is optimal from the point of view of cognitive clarity criteria defined in the previous part of the study, is proposed. To this end, methods have been developed to formalize several nontrivial criteria of cognitive clarity. Also, a decision rule has been introduced for choosing the optimal visual image. An example of applying the proposed metaphor, confirming its efficiency and effectiveness, is given.

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Section: Development Of Fcm Visualization Metaphor: Basic Conceptmentioning

confidence: 99%

Section: Fig 9 Fcm Visual Imagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Constructing Optimal Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

Podvesovskii¹,

Isaev²

2019

Self Cite

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“…There are two extremes in the improvement of the efficiency of information collection: to make all the work fully automatic, thereby saving on human resources, or to make process control completely manual. In this paper, an "intermediate" version is considered when an expert is engaged in evaluating the effectiveness of the collection process, but due to the use of various tools, such as visualization, his work time is significantly reduced [9]. In addition, the following approaches are used in the developed software package to optimize information collection before analysis: 1) refinement of search queries; 2) ignoring duplicate information;…”

Section: Fig 1 Overview Diagram Of Collecting and Processing Datamentioning

confidence: 99%

Approaches to visualizing big text data at the stage of collection and pre-processing

Makarova¹,

Lagerev²,

Lozbinev³

2019

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This paper describes the text data analysis in the course of management decision making. We examine in detail the process of collection of text data for further analysis and the use of imaging to increase the efficiency of human resources during collection and data preprocessing. A modification of the algorithm for creating an "n-gram cloud" visualization is proposed, which makes visualization accessible to people with visual impairments. Also, a method of visualization of n-gram vector representation models (word embedding) is proposed. On the basis of the conducted research, a part of a software package was implemented, which is responsible for creating interactive visualizations in a browser and interoperating with them.

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“…At the same time, the solution of multi-criteria decision making problems by means of these methods can be not very effective when there are dozens and hundreds of alternatives, and they all have more than a dozen criteria. These situations are quite common when, for example, the source of alternatives is multisensory systems, or when initially many alternatives are formed by means of specialized systems in the course of multiple simulation [2,3].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Support and Software of Visual Filtering of Alternatives in Multi-criteria Decision Making Problems

Zakharova¹,

Zaharova

Коростелёв³

et al. 2019

GraphiCon'2019 Proceedings. Volume 2

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The article deals with the problem of multi-criteria decision-making problems, which are characterized by a large number of options and alternatives. It is proposed to use visual filtering of graphic images describing the corresponding alternatives as one of the stages in decision-making in such tasks. The approaches and requirements for the construction of graphic images of alternatives are considered. Describes the steps and algorithms for constructing visual images of alternatives, based on the radial and pie charts, and include the normalization procedure. It describes software that implements the proposed algorithms, as well as providing interactive interaction with an expert for visual filtering of multi-criteria alternatives. Additionally, the capabilities of the developed software are described, which include filtering alternatives based on threshold values, as well as the possibility of conducting a series of experiments in order to obtain the union or intersection of filtered sets of alternatives. A synthetic test for filtering 201 alternatives is described, each of which is described by 15 criteria. As a result of a series of experiments, this choice set was reduced by about 28 times. A description is also given of an experiment on visual filtering of real alternatives that describe estimates of the accuracy of calculating inviscid flow around a cone using several OpenFoam solvers. Each solver is characterized by 288 criteria, and according to the results of visual filtering, the advantage in the accuracy of the calculations of two solvers over the others is clearly established.

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Visualization Metaphors for Fuzzy Cognitive Maps

Cited by 19 publications

References 0 publications

Constructing Optimal Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

Constructing Optimal Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

Approaches to visualizing big text data at the stage of collection and pre-processing

Mathematical Support and Software of Visual Filtering of Alternatives in Multi-criteria Decision Making Problems

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