scholarly journals Depicting More Information in Enriched Squarified Treemaps with Layered Glyphs

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 123
Author(s):  
Anderson Gregório Marques Soares ◽  
Elvis Thermo Carvalho Miranda ◽  
Rodrigo Santos do Amor Divino Lima ◽  
Carlos Gustavo Resque dos Santos ◽  
Bianchi Serique Meiguins
Keyword(s):  
Information Visualization ◽  
Data Structures ◽  
User Study ◽  
Relevant Information ◽  
Data Representation ◽  
High Dimensional ◽  
Hierarchical Data ◽  
Visual Data ◽  
Visual Variable ◽  
Data Clusters

The Treemap is one of the most relevant information visualization (InfoVis) techniques to support the analysis of large hierarchical data structures or data clusters. Despite that, Treemap still presents some challenges for data representation, such as the few options for visual data mappings and the inability to represent zero and negative values. Additionally, visualizing high dimensional data requires many hierarchies, which can impair data visualization. Thus, this paper proposes to add layered glyphs to Treemap’s items to mitigate these issues. Layered glyphs are composed of N partially visible layers, and each layer maps one data dimension to a visual variable. Since the area of the upper layers is always smaller than the bottom ones, the layers can be stacked to compose a multidimensional glyph. To validate this proposal, we conducted a user study to compare three scenarios of visual data mappings for Treemaps: only Glyphs (G), Glyphs and Hierarchy (GH), and only Hierarchy (H). Thirty-six volunteers with a background in InfoVis techniques, organized into three groups of twelve (one group per scenario), performed 8 InfoVis tasks using only one of the proposed scenarios. The results point that scenario GH presented the best accuracy while having a task-solving time similar to scenario H, which suggests that representing more data in Treemaps with layered glyphs enriched the Treemap visualization capabilities without impairing the data readability.

Relativity and Resolution for High Dimensional Information Visualization with Generalized Association Plots (GAP)

Compstat ◽  
2002 ◽  
pp. 55-66 ◽  
Author(s):  
Shun-Chuan Chang ◽  
Chun-houh Chen ◽  
Yueh-Yun Chi ◽  
Chih-Wen Ouyoung
Keyword(s):  
Information Visualization ◽  
High Dimensional

scholarly journals Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

2019 ◽  
Author(s):  
Runpu Chen ◽  
Le Yang ◽  
Steve Goodison ◽  
Yijun Sun
Keyword(s):  
Deep Learning ◽  
Cluster Structure ◽  
Data Representation ◽  
Supplementary Information ◽  
High Dimensional ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Subtypes ◽  
Novel Approach ◽  
Open Source Software Package

Abstract Motivation Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes. Results To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data. Availability and implementation An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html. Supplementary information Supplementary data are available at Bioinformatics online.

Analysis of Design Pattern to Develop Object-Oriented Systems

2019 ◽  
Vol 10 (3) ◽  
pp. 1-9
Author(s):  
Komal M. Birare
Keyword(s):  
Problem Solving ◽  
Information Visualization ◽  
Design Pattern ◽  
Design Patterns ◽  
Object Oriented ◽  
Data Representation ◽  
Engineering Problem ◽  
Visualization Software ◽  
Structure Factors ◽  
Object Oriented Systems

This article is an introduction to design patterns. Patterns are recent software engineering problem-solving discipline that emerged from the object-oriented community. The primary purpose of the pattern is communicating design insights and making patterns coherent and easier to understand. On the basis of a review of existing frameworks and the authors own experiences building visualization software, they present a series of design patterns for the domain of information visualization. The authors discuss the structure, factors use, and association of patterns bridge of data representation, graphics, and interaction.

Sign in / Sign up

Export Citation Format

Share Document