Disk Diagram: An Interactive Visualization Technique of Fuzzy Set Operations for the Analysis of Fuzzy Data

2010 ◽  
Vol 9 (3) ◽  
pp. 220-232 ◽  
Author(s):  
Yeseul Park ◽  
Jinah Park
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Visual Analytics ◽  
Interactive Visualization ◽  
Data Sets ◽  
Fuzzy Data ◽  
Visualization Technique ◽  
Data Set ◽  
Set Operations ◽  
Data Elements

Fuzzy set refers to the data set which does not have separate, distinct clusters, and they contain data elements whose membership degrees are between 0.0 and 1.0. Many fuzzy sets exist in the real world, and one of the important issues is to make a decision from the fuzzy sets using visual analytics tools by extracting information in the data set intuitively. To analyze the element data in fuzzy sets, the visualization of fuzzy sets needs to show an overview of the data with membership degree and the relationship among the sets. In this article, we suggest an interactive visualization technique of fuzzy set operations, called Disk Diagram, which offers distribution of fuzzy data and two scenarios to allow users to interpret inter-dependency among fuzzy sets. A Disk Diagram enables to depict complexity of fuzzy sets by showing the degree of resemblance between the sets with the layout of star coordinates. This article describes the use of a Disk Diagram with two different data sets such as fuzzy disease set and terror related words set. Lastly, we report the results of heuristic evaluation to show that our technique supports visual perception, usability, and knowledge discovery process in the areas of visual representation and interaction.

Visual Analytics in Ship Performance and Navigation Information for Sensor Specific Fault Detection

2017 ◽  
Author(s):  
Lokukaluge P. Perera ◽  
Brage Mo
Keyword(s):  
Fault Detection ◽  
Visual Analytics ◽  
Domain Knowledge ◽  
Data Sets ◽  
Data Set ◽  
Navigation Data ◽  
Hidden Data ◽  
Ship Performance ◽  
Navigation Information ◽  
Erroneous Data

Ocean internet of things (IoT - onboard and onshore) collects big data sets of ship performance and navigation information under various data handling processes. That extract vessel performance and navigation information that are used for ship energy efficiency and emission control applications. However, the quality of ship performance and navigation data can play an important role in such applications, where sensor faults may introduce various erroneous data regions and that may degrade to the outcome. This study proposes visual analytics, where hidden data patterns, clusters, correlations and other useful information are visually from the respective data set extracted, to identify such erroneous data regions. The domain knowledge (i.e. ship performance and navigation conditions) has also been used to interpret such erroneous data regions and identify the respective sensors that relate to the same situations. Finally, a ship performance and navigation data set of a selected vessel is analyzed to identify erroneous data regions for three selected sensor fault situations (i.e. wind, log speed and draft sensors) under the proposed visual analytics. Hence, this approach can be categorized as a sensor specific fault detection methodology by considering the same results.

scholarly journals Fuzzy sets and presheaves

2019 ◽  
Vol 1 ◽  
pp. 3
Author(s):  
John F. Jardine
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
General Framework ◽  
Theoretic Approach ◽  
Data Set

This paper presents a presheaf theoretic approach to the construction of fuzzy sets, which builds on Barr's description of fuzzy sets as sheaves of monomorphisms on a locale. Presheaves are used to give explicit descriptions of limit and colimit descriptions in fuzzy sets on an interval. The Boolean localization construction for sheaves on a locale specializes to a theory of stalks for sheaves and presheaves on an interval.The system V∗(X) of Vietoris-Rips complexes for a data set X is both a simplicial fuzzy set and a simplicial sheaf in this general framework. This example is explicitly discussed through a series of examples.

LIVE: A Work-Centered Approach to Support Visual Analytics of Multi-Dimensional Engineering Design Data With Interactive Visualization and Data-Mining

2011 ◽  
Author(s):  
Xin Yan ◽  
Mu Qiao ◽  
Timothy W. Simpson ◽  
Jia Li ◽  
Xiaolong Luke Zhang
Keyword(s):  
Data Analysis ◽  
Engineering Design ◽  
Visual Analytics ◽  
Interactive Visualization ◽  
User Interaction ◽  
Data Sets ◽  
Preliminary Evaluation ◽  
Complex Data ◽  
Design Data ◽  
Depth Analysis

During the process of trade space exploration, information overload has become a notable problem. To find the best design, designers need more efficient tools to analyze the data, explore possible hidden patterns, and identify preferable solutions. When dealing with large-scale, multi-dimensional, continuous data sets (e.g., design alternatives and potential solutions), designers can be easily overwhelmed by the volume and complexity of the data. Traditional information visualization tools have some limits to support the analysis and knowledge exploration of such data, largely because they usually emphasize the visual presentation of and user interaction with data sets, and lack the capacity to identify hidden data patterns that are critical to in-depth analysis. There is a need for the integration of user-centered visualization designs and data-oriented data analysis algorithms in support of complex data analysis. In this paper, we present a work-centered approach to support visual analytics of multi-dimensional engineering design data by combining visualization, user interaction, and computational algorithms. We describe a system, Learning-based Interactive Visualization for Engineering design (LIVE), that allows designer to interactively examine large design input data and performance output data analysis simultaneously through visualization. We expect that our approach can help designers analyze complex design data more efficiently and effectively. We report our preliminary evaluation on the use of our system in analyzing a design problem related to aircraft wing sizing.

scholarly journals DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data

2008 ◽  
Vol 7 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Niklas Elmqvist ◽  
John Stasko ◽  
Philippas Tsigas
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Set ◽  
Data Dependencies ◽  
Expert Review ◽  
History Of ◽  
Multidimensional Data Sets ◽  
High Degree

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

scholarly journals A Proposal for an Austrian Nursing Minimum Data Set (NMDS)

2014 ◽  
Vol 05 (02) ◽  
pp. 538-547 ◽  
Author(s):  
W.O. Hackl ◽  
E. Ammenwerth ◽  
R. Ranegger
Keyword(s):  
Delphi Study ◽  
Medical Condition ◽  
Group Discussion ◽  
Minimum Data Set ◽  
Data Sets ◽  
Nursing Assessment ◽  
Data Set ◽  
Nursing Outcomes ◽  
Minimum Data ◽  
Data Elements

SummaryObjective: Nursing Minimum Data Sets can be used to compare nursing care across clinical populations, settings, geographical areas, and time. NMDS can support nursing research, nursing management, and nursing politics. However, in contrast to other countries, Austria does not have a unified NMDS. The objective of this study is to identify possible data elements for an Austrian NMDS.Methods: A two-round Delphi survey was conducted, based on a review of available NMDS, 22 expert interviews, and a focus group discussion.Results: After reaching consensus, the experts proposed the following 56 data elements for an NMDS: six data elements concerning patient demographics, four data elements concerning data of the healthcare institution, four data elements concerning patient’s medical condition, 20 data elements concerning patient problems (nursing assessment, nursing diagnoses, risk assessment), eight data elements concerning nursing outcomes, 14 data elements concerning nursing interventions, and no additional data elements concerning nursing intensity.Conclusion: The proposed NMDS focuses on the long-term and acute care setting. It must now be implemented and tested in the nursing practice.Citation: Ranegger R, Hackl WO, Ammenwerth E. A proposal for an austrian nursing minimum data set (NMDS): A Delphi study. Appl Clin Inf 2014; 5: 538–547 http://dx.doi.org/10.4338/ACI-2014-04-RA-0027

scholarly journals A COMPARATIVE ANALYSIS OF K-MEANS AND HIERARCHICAL CLUSTERING

2021 ◽  
pp. 412-418
Author(s):  
Aastha Gupta ◽  
Himanshu Sharma ◽  
Anas Akhtar
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Analytical Techniques ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
The Many ◽  
Data Elements

Clustering is the process of arranging comparable data elements into groups. One of the most frequent data mining analytical techniques is clustering analysis; the clustering algorithm’s strategy has a direct influence on the clustering results. This study examines the many types of algorithms, such as k-means clustering algorithms, and compares and contrasts their advantages and disadvantages. This paper also highlights concerns with clustering algorithms, such as time complexity and accuracy, in order to give better outcomes in a variety of environments. The outcomes are described in terms of big datasets. The focus of this study is on clustering algorithms with the WEKA data mining tool. Clustering is the process of dividing a big data set into small groups or clusters. Clustering is an unsupervised approach that may be used to analyze big datasets with many characteristics. It’s a data-modeling technique that provides a clear image of your data. Two clustering methods, k-means and hierarchical clustering, are explained in this survey and their analysis using WEKA tool on different data sets. KEYWORDS: data clustering, weka , k-means, hierarchical clustering

scholarly journals N-Fuzzy Sets and Their Applications

2021 ◽  
Author(s):  
Hariwan Z. Ibrahim
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Medical Diagnosis ◽  
Euclidean Distance ◽  
Score Function ◽  
Intuitionistic Fuzzy Sets ◽  
Accuracy Function ◽  
Pythagorean Fuzzy Sets ◽  
Intuitionistic Fuzzy ◽  
Set Operations

Abstract The purpose of this paper is to define n-Fuzzy sets and study their relationship with intuitionistic fuzzy sets, Pythagorean fuzzy sets and Fermatean fuzzy sets. The n-Fuzzy sets can deal with more uncertain situations than intuitionistic fuzzy sets, Pythagorean fuzzy sets and Fermatean fuzzy sets because of its larger range of describing the membership grades. The set operations, score function, accuracy function and Euclidean distance of n-Fuzzy sets will study. Finally, we study the Sanchez$^{,}$s approach for medical diagnosis and extend this concept with the notion of n-Fuzzy set.

A NEW DISTANCE FOR INTERVAL TYPE-2 FUZZY SETS WITH AN APPLICATION TO CLUSTERING

2014 ◽  
Vol 13 (04) ◽  
pp. 1450020
Author(s):  
ARMAGHAN HEIDARZADE ◽  
NEZAM MAHDAVI-AMIRI ◽  
IRAJ MAHDAVI
Keyword(s):  
Fuzzy Sets ◽  
Clustering Algorithm ◽  
Real Data ◽  
Data Sets ◽  
Fuzzy Data ◽  
Fuzzy Membership Functions ◽  
Definition Of ◽  
Interval Type ◽  
Distance Formula

Type-2 fuzzy sets are generalizations of ordinary fuzzy sets, in which membership grades are characterized by fuzzy membership functions. Here, a problem of finding distance between two interval type-2 fuzzy sets (IT2-FSs) was considered. Based on a new definition of centroid for an IT2-FS, a formulation for calculation of the distance between two IT2-FSs was introduced, and an algorithm was explained to obtain it. The proposed distance formula was incorporated in Yang and Shih's clustering algorithm to reach a clustering method for interval type-2 fuzzy data sets. The applicability of the proposed distance formula was evaluated using two artificial and real data sets, and reasonable results were obtained.

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