Visualizing Design Spaces Using Two-Dimensional Contextual Self-Organizing Maps

Richardson, Trevor; Nekolny, Brett; Holub, Joseph; Winer, Eliot H.

doi:10.2514/1.j052181

Cited by 6 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One way to better understand the decision making of participants is to visualize the design space by placing all ideas on a map and grouping similar items together. Design space exploration techniques [26] were developed to visualize a design space and generate feasible designs. Motivated by the fact that humans essentially think in two or three dimensions, many methods to visualize high dimensional data by mapping it to lower dimension manifolds have been studied extensively [27][28][29].…”

Section: Design Space Visualizationmentioning

confidence: 99%

Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel

Ahmed

Ramachandran

Fuge

et al. 2018

Journal of Mechanical Design

View full text Add to dashboard Cite

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets to score ideas on their uniqueness. However, their decisions about novelty are often subjective and difficult to explain. In this paper, we demonstrate a way to uncover human judgment of design idea similarity using two-dimensional (2D) idea maps. We derive these maps by asking participants for simple similarity comparisons of the form “Is idea A more similar to idea B or to idea C?” We show that these maps give insight into the relationships between ideas and help understand the design domain. We also propose that novel ideas can be identified by finding outliers on these idea maps. To demonstrate our method, we conduct experimental evaluations on two datasets—colored polygons (known answer) and milk frother sketches (unknown answer). We show that idea maps shed light on factors considered by participants in judging idea similarity and the maps are robust to noisy ratings. We also compare physical maps made by participants on a white-board to their computationally generated idea maps to compare how people think about spatial arrangement of design items. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational methods.

show abstract

Section: Design Space Visualizationmentioning

confidence: 99%

Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel

Ahmed

Ramachandran

Fuge

et al. 2018

Journal of Mechanical Design

View full text Add to dashboard Cite

show abstract

“…One way to better understand the decision making of raters is to visualize the design space by placing all ideas on a map and grouping similar items together. Design space exploration techniques [19] have been developed to visualize a design space and generate feasible designs. Motivated by the fact that humans essentially think in two or three dimensions, many methods to visualize high dimensional data by mapping it to lower dimension manifolds have been studied extensively [20,21].…”

Section: Design Space Visualizationmentioning

confidence: 99%

Unpacking Subjective Creativity Ratings: Using Embeddings to Explain and Measure Idea Novelty

Ahmed

Fuge

Hunter

et al. 2018

Volume 7: 30th International Conference on Design Theory and Methodology

View full text Add to dashboard Cite

Assessing similarity between design ideas is an inherent part of many design evaluations to measure novelty. In such evaluation tasks, humans excel at making mental connections among diverse knowledge sets and scoring ideas on their uniqueness. However, their decisions on novelty are often subjective and difficult to explain. In this paper, we demonstrate a way to uncover human judgment of design idea similarity using two dimensional idea maps. We derive these maps by asking humans for simple similarity comparisons of the form “Is idea A more similar to idea B or to idea C?” We show that these maps give insight into the relationships between ideas and help understand the domain. We also propose that the novelty of ideas can be estimated by measuring how far items are on these maps. We demonstrate our methodology through the experimental evaluations on two datasets of colored polygons (known answer) and milk frothers (unknown answer) sketches. We show that these maps shed light on factors considered by raters in judging idea similarity. We also show how maps change when less data is available or false/noisy ratings are provided. This method provides a new direction of research into deriving ground truth novelty metrics by combining human judgments and computational methods.

show abstract

“…The Pareto optimal solution visualization method using the SOM has also been investigated in [2], [8]- [11]. Applications of the SOM to the optimization, which includes the single objective optimization, and related methods are reviewed in [12]. However, the solution representation capability has not been studied quantitatively and in detail to the authors' knowledge.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

Hironaka

Okamoto

Koakutsu

et al. 2015

SICE Journal of Control, Measurement, and System Integration

View full text Add to dashboard Cite

: In the multi-objective optimization problem that appears naturally in the decision making process for the complex system, the visualization of the innumerable solutions called Pareto optimal solutions is important issue. This paper focuses on the Pareto optimal solution visualization method using the self-organizing maps which is one of promising visualization methods. The method has advantages in grasping the overall structure of the solutions and comparing the objective functions simultaneously. This method has been applied to some real problems, but its solution representation capability has not been studied well. This paper investigates the solution representation capability of the Pareto optimal solution visualization method using the SOM and points out its shortcomings. Then, two improvements are introduced to the visualization method. The effectiveness of the proposed method is confirmed via comparing it to the conventional visualization method using three indices evaluating the solution representation capability.

show abstract

Visualizing Design Spaces Using Two-Dimensional Contextual Self-Organizing Maps

Cited by 6 publications

References 17 publications

Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel

Interpreting Idea Maps: Pairwise Comparisons Reveal What Makes Ideas Novel

Unpacking Subjective Creativity Ratings: Using Embeddings to Explain and Measure Idea Novelty

Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps

Contact Info

Product

Resources

About