scholarly journals Biases with the Generalized Euclidean Distance measure in disparity analyses with high levels of missing data

Palaeontology ◽  
2019 ◽  
Vol 62 (5) ◽  
pp. 837-849 ◽  
Author(s):  
Oscar E. R. Lehmann ◽  
Martín D. Ezcurra ◽  
Richard J. Butler ◽  
Graeme T. Lloyd
Keyword(s):  
Missing Data ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Euclidean Distance Measure

Image Retrieval of Self-Adapt Distance Measure Based on SLLE

2014 ◽  
Vol 989-994 ◽  
pp. 3675-3678
Author(s):  
Xiao Fen Wang ◽  
Hai Na Zhang ◽  
Xiu Rong Qiu ◽  
Jiang Ping Song ◽  
Ke Xin Zhang
Keyword(s):  
Image Retrieval ◽  
High Dimension ◽  
Topological Structure ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Content Based Image Retrieval ◽  
Locally Linear Embedding ◽  
Euclidean Distance Measure ◽  
Linear Embedding ◽  
Dimension Space

Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.

A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem

2020 ◽  
Vol 28 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Rodrigo Naranjo ◽  
Matilde Santos ◽  
Luis Garmendia
Keyword(s):  
Pattern Recognition ◽  
Fuzzy Number ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Trading System ◽  
Weighted Distance ◽  
Pattern Recognition Problem ◽  
Euclidean Distance Measure ◽  
Desirable Behavior

A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance between two FSNs is larger when they are in different GTFNs, and smaller when they are in the same GTFN. The advantage of the new measure is demonstrated on a fuzzy forecasting trading system over two different real stock markets, which provides better predictions with larger profits than those obtained using the Euclidean distance measure for the same system.

scholarly journals Clustering of OECD Countries Out of Pocket Health Expenditure Time Series Data

2016 ◽  
Vol 8 (2) ◽  
pp. 23
Author(s):  
Songul Cinaroglu
Keyword(s):  
Euclidean Distance ◽  
Time Series Data ◽  
Distance Measure ◽  
Health Expenditures ◽  
Oecd Countries ◽  
Distance Measures ◽  
Series Data ◽  
Longest Common Subsequences ◽  
Study Results ◽  
Euclidean Distance Measure

<p>Out of pocket health expenditures points out to the payments made by households at the point<br />they receive health services. Frequently these include doctor consultation fees, purchase of<br />medication and hospital bills. In this study hierarchical clustering method was used for<br />classification of 34 countries which are members of OECD (Organization for Economic<br />Cooperation and Development) in terms of out of pocket health expenditures for the years<br />between 1995-2011. Longest common subsequences (LCS), correlation coefficient and<br />Euclidean distance measure was used as a measure of similarity and distance in hierarchical<br />clustering. At the end of the analysis it was found that LCS and Euclidean distance measures<br />were the best for determining clusters. Furthermore, study results led to understand grouping<br />of OECD countries according to health expenditures.</p>

scholarly journals A Comparison of Learning and Teaching Styles - Self-Perception of IT Students

10.28945/2826 ◽  
2004 ◽  
Author(s):  
Zlatko J Kovacic
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Teaching Styles ◽  
Learning Preferences ◽  
The Self ◽  
Self Perception ◽  
Learning And Teaching ◽  
Euclidean Distance Measure

This paper presents the results of an empirical study of the learning styles of a group of computing students and the teaching styles of their tutors at The Open Polytechnic of New Zealand. This study of learning styles is based on Kolb’s learning model and the Felder-Soloman learning style instrument. To identify how close students’ learning styles match the teaching styles of their tutors we have used two indicators: the self-perception of the students about the proximity of their learning styles and the teaching styles of their tutors and a Euclidean distance measure between students’ and tutors’ learning preferences. Using survey data and the learning styles instrument results we identify the differences between the learning and teaching styles. Both indicators show consistent and significant differences between the learning and teaching styles, in the way students and their tutors perceive and understand information. Finally we make recommendation to tutors on how to bridge this gap and address the learning styles of their students.

Approximation Methods in Discrete Spatial Density Analysis for Finite Populations

1992 ◽  
Vol 74 (3) ◽  
pp. 867-873 ◽  
Author(s):  
Frank O'Brien
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Spatial Density ◽  
Human Populations ◽  
Finite Sample ◽  
Root Model ◽  
Euclidean Distance Measure ◽  
Finite Sample Size ◽  
Density Analysis

The author's three-parameter square-root model for the measurement of discrete spatial density in human populations was previously derived under the assumption that exact coordinate locations of the density points were available. The model, called the population density index (PDI) model, has been expanded to include a set of routines for calculating two-dimensional spatial density measures based upon in situ geometric approximations of the interobject Euclidean distance measure for any finite sample size. The derivation and specification of the algorithm for the abbreviated calculation routines are presented and exemplified. The author has been able to apply the methods of the PDI model to submarine environments at the U.S. Naval Underwater Systems Center, resulting in several U.S. Patent applications.

scholarly journals Taxonomic Significance of Glume Morphology and Leaf Epidermal Characteristics in some Taxa of Tribe Aveneae (Poaceae)

2013 ◽  
Vol 5 (2) ◽  
pp. 144-155 ◽  
Author(s):  
Adel EL-GAZZAR ◽  
Monier El-GHANI ◽  
Lamiaa SHALABI
Keyword(s):  
Euclidean Distance ◽  
Distance Measure ◽  
Data Matrix ◽  
Numerical Classification ◽  
Taxonomic Significance ◽  
Clustering Method ◽  
Euclidean Distance Measure ◽  
Pca Method ◽  
Silica Skeleton

The numerical classification of tribe Aveneae (Poaceae) is discussed regarding the glume morphology and silica skeleton morphologies. The present study dealt with 18 species belonging to 10 genera of the tribe to cover as many groups as possible within Aveneae. The total of 31 structural characters and 71 character states were scored comparatively. The resulted data matrix was analyzed under a combination of Euclidean distance measure and Ward’s clustering method included in the program package PC-ORD version 5. The resulted dendrogram separated the tribe into five basic sub-ordinate groups created from three major groups A, B and C. The taxonomic significance of these results was discussed. The results showed congruence between the clustering and PCA method, in suggesting three major groups and 5 sub-ordinate groups.

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