Female breast shape categorization based on analysis of CAESAR 3D body scan data

2018 ◽  
Vol 89 (4) ◽  
pp. 590-611 ◽  
Author(s):  
Jie Pei ◽  
Huiju Park ◽  
Susan P. Ashdown
Keyword(s):  
Goodness Of Fit ◽  
Misclassification Rate ◽  
Clustering Methods ◽  
Cluster Number ◽  
Female Breast ◽  
Multiple Data ◽  
Intimate Apparel ◽  
Breast Shape ◽  
Scan Data ◽  
Relative Measurements

In this study we explore the variation in female breast shape across the younger (age: 18–45), non-obese (BMI < 30) North American Caucasian population, a population that has not previously been well-represented in studies of breast shape. A method of classifying breast shape was developed based on multiple data-mining techniques. Forty-one relative measurements (i.e., ratios and angles) were constructed from 66 raw measurements (circumferences, depths, widths, etc.), extracted from 478 CAESAR (Civilian American and European Surface Anthropometry Resource) scans, using self-developed Matlab® programs. Seventy subjects were regarded as outliers and were removed. The remaining data were transformed and standardized to ensure robust analysis. To judge results, an algorithm was developed to visualize clustering outcomes in the form of side profiles of breasts. The results of three clustering methods, namely hierarchical, K-means, and K-medoids clustering, were compared. Finally, breast shapes were categorized into three and five groups by two different cluster number selection criteria proposed by the study: (1) based on misclassification rate; (2) based on the goodness-of-fit of the model. Several of the relative body measurements were identified to be critical in defining breast shape. The findings and the proposed methods of this study can contribute to the development of improved shape and sizing systems of bra products that work for both manufacturers and consumers. The new methodology developed in this study can also be applied to other types of intimate apparel products where an understanding of body shape plays a key role in body support, comfort, and fit.

EdClust: A heuristic sequence clustering method with higher sensitivity

2021 ◽  
Author(s):  
Ming Cao ◽  
Qinke Peng ◽  
Ze-Gang Wei ◽  
Fei Liu ◽  
Yi-Fan Hou
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Sequencing Data ◽  
Clustering Method ◽  
Cluster Number ◽  
Sequence Clustering ◽  
Downstream Analysis ◽  
Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

Study on Body Foundation of Brassiere Steel Ring Shapes

2011 ◽  
Vol 331 ◽  
pp. 101-104
Author(s):  
Su Zhen Liang
Keyword(s):  
Three Dimensional ◽  
Material Design ◽  
Pattern Design ◽  
Body Scanning ◽  
The Core ◽  
Female Breast ◽  
Core Technology ◽  
Breast Shape ◽  
Design And Manufacture ◽  
The Relationship

The pattern design of brassieres is the core technology for the design and manufacture of brassieres, while the female breast shape and part dimensions are the foundations for pattern design of brassieres. Based upon 3D body scanning, this paper studied the relationship between the breast root shape and the steel ring by considering the features of the pattern design of the brassiere. It concludes that the breast root girth is a complicated three-dimensional curve; it’s inappropriate for the neighboring size’s brassieres to adopt the steel ring with the same specification; the material design of the steel ring should be moderate. The purpose is to provide human body basis for pattern design of brassieres and achieve more standard and scientific pattern design of the brassiere by the underwear enterprises.

A Skiing Trace Clustering Model for Injury Risk Assessment

2016 ◽  
Vol 8 (1) ◽  
pp. 56-68 ◽  
Author(s):  
Milan Dobrota ◽  
Boris Delibašić ◽  
Pavlos Delias
Keyword(s):  
Injury Risk ◽  
Activity Patterns ◽  
Risk Level ◽  
Risk Category ◽  
Clustering Methods ◽  
Cluster Number ◽  
Event Logs ◽  
Clustering Model ◽  
Initial Transformation ◽  
Process Event

This paper investigates the relation between skiing movement activity patterns and risk of injury. The goal is to provide a framework which can be used for estimating the level of skiers' injury risks, based on skiing patterns. Data, collected from ski-lift gates in the form of process event logs is analyzed. After initial transformation of data into traces, trace vectors, and similarity matrix, using several clustering methods different skiing patterns are identified and compared. The quality of clusters is determined by how well clusters discriminate between injured and noninjured skiers. The goal was to achieve the best possible discrimination. Several experimental settings were made to achieve and suggest a good combination of algorithm parameters and cluster number. After clusters are obtained, they are categorized in three categories according to risk level. It can be concluded that the proposed method can be used to distinguish skiing patterns by risk category based on injury occurrences.

Hard c-Means Using Quadratic Penalty-Vector Regularization for Uncertain Data

2012 ◽  
Vol 16 (7) ◽  
pp. 831-840 ◽  
Author(s):  
Yasunori Endo ◽  
◽  
Arisa Taniguchi ◽  
Yukihiro Hamasuna ◽  
◽  
...  
Keyword(s):  
Missing Values ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Uncertain Data ◽  
Unsupervised Classification ◽  
Real Space ◽  
Clustering Methods ◽  
Cluster Number ◽  
Numerical Examples ◽  
Classification Technique

Clustering is an unsupervised classification technique for data analysis. In general, each datum in real space is transformed into a point in a pattern space to apply clustering methods. Data cannot often be represented by a point, however, because of its uncertainty, e.g., measurement error margin and missing values in data. In this paper, we will introduce quadratic penalty-vector regularization to handle such uncertain data using Hard c-Means (HCM), which is one of the most typical clustering algorithms. We first propose a new clustering algorithm called hard c-means using quadratic penalty-vector regularization for uncertain data (HCMP). Second, we propose sequential extraction hard c-means using quadratic penalty-vector regularization (SHCMP) to handle datasets whose cluster number is unknown. Furthermore, we verify the effectiveness of our proposed algorithms through numerical examples.

scholarly journals A Contrast of Guideline Recommendations and Tullis's Prediction Model for Computer Displays: Should Text be Left-Justified?

1987 ◽  
Vol 31 (11) ◽  
pp. 1226-1228
Author(s):  
Gail A. Fontenelle
Keyword(s):  
Prediction Model ◽  
Information Extraction ◽  
Search Time ◽  
Guideline Recommendation ◽  
Subjective Ratings ◽  
Multiple Data ◽  
Scan Data ◽  
Types Of Information

Two experiments investigated the effect of layout complexity for performance at varying levels of practice on four types of information extraction tasks. Layout complexity is defined as the number of unique horizontal and vertical starting positions of items in the display (Tullis, 1984). In the first study, layout complexity was manipulated by either left-justifying or not left-justifying text. In the second study, subject veiwed a third experimental screen that displayed the starting positions of items in a completely unpredictable pattern. Moderate violations of the typical guideline recommendation that alphanumeric data be left-justified did not increase user search time across all four tasks in either the first or second study. Furthermore, severe violations of the recommendation did not increase user search time for three tasks (find label, scan data, and compare label). However when subjects compared multiple data values, the random format did increase user search time. Though performance using the three experimental screens was comparable across the four tasks with only one exception, subjective ratings demonstrated differences between the three formats.

scholarly journals A Nonparametric Model for Multi-Manifold Clustering with Mixture of Gaussians and Graph Consistency

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 830 ◽  
Author(s):  
Xulun Ye ◽  
Jieyu Zhao ◽  
Yu Chen
Keyword(s):  
Dirichlet Process ◽  
Nearest Neighbor ◽  
Nonparametric Model ◽  
Clustering Methods ◽  
K Nearest Neighbor ◽  
Cluster Number ◽  
Mixture Of Gaussians ◽  
Neighbor Graph ◽  
Nearest Neighbor Graph ◽  
Manifold Clustering

Multi-manifold clustering is among the most fundamental tasks in signal processing and machine learning. Although the existing multi-manifold clustering methods are quite powerful, learning the cluster number automatically from data is still a challenge. In this paper, a novel unsupervised generative clustering approach within the Bayesian nonparametric framework has been proposed. Specifically, our manifold method automatically selects the cluster number with a Dirichlet Process (DP) prior. Then, a DP-based mixture model with constrained Mixture of Gaussians (MoG) is constructed to handle the manifold data. Finally, we integrate our model with the k-nearest neighbor graph to capture the manifold geometric information. An efficient optimization algorithm has also been derived to do the model inference and optimization. Experimental results on synthetic datasets and real-world benchmark datasets exhibit the effectiveness of this new DP-based manifold method.

Classification of Breast Shape of Women Aged 11~15 Using 3D Body Scan Data

2017 ◽  
Vol 19 (6) ◽  
pp. 786-794 ◽  
Author(s):  
Tingting Han ◽  
Hwa Kyung Song ◽  
Kyu Sun Lee
Keyword(s):  
Body Scan ◽  
Breast Shape ◽  
Scan Data

scholarly journals Coupled co-clustering-based unsupervised transfer learning for the integrative analysis of single-cell genomic data

2020 ◽  
Author(s):  
Pengcheng Zeng ◽  
Jiaxuan Wangwu ◽  
Zhixiang Lin
Keyword(s):  
Single Cell ◽  
Transfer Learning ◽  
Genomic Data ◽  
Integrative Analysis ◽  
Data Sets ◽  
Clustering Methods ◽  
Genomic Features ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Cell Data

Abstract Unsupervised methods, such as clustering methods, are essential to the analysis of single-cell genomic data. The most current clustering methods are designed for one data type only, such as single-cell RNA sequencing (scRNA-seq), single-cell ATAC sequencing (scATAC-seq) or sc-methylation data alone, and a few are developed for the integrative analysis of multiple data types. The integrative analysis of multimodal single-cell genomic data sets leverages the power in multiple data sets and can deepen the biological insight. In this paper, we propose a coupled co-clustering-based unsupervised transfer learning algorithm (coupleCoC) for the integrative analysis of multimodal single-cell data. Our proposed coupleCoC builds upon the information theoretic co-clustering framework. In co-clustering, both the cells and the genomic features are simultaneously clustered. Clustering similar genomic features reduces the noise in single-cell data and facilitates transfer of knowledge across single-cell datasets. We applied coupleCoC for the integrative analysis of scATAC-seq and scRNA-seq data, sc-methylation and scRNA-seq data and scRNA-seq data from mouse and human. We demonstrate that coupleCoC improves the overall clustering performance and matches the cell subpopulations across multimodal single-cell genomic datasets. Our method coupleCoC is also computationally efficient and can scale up to large datasets. Availability: The software and datasets are available at https://github.com/cuhklinlab/coupleCoC.

Sign in / Sign up

Export Citation Format

Share Document