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.

scholarly journals Classification of the Leguminosae-Papilionoideae: A Numerical Re-assessment

2013 ◽  
Vol 5 (4) ◽  
pp. 499-507 ◽  
Author(s):  
Adel EL-GAZZAR ◽  
Monier EL-GHANI ◽  
Nahed EL-HUSSEINI ◽  
Adel KHATTAB
Keyword(s):  
Cluster Analysis ◽  
Distance Measure ◽  
Morphological Characters ◽  
Data Matrix ◽  
Clustering Method ◽  
Lower Rank ◽  
Phylogenetic Studies

The subdivision of the Leguminosae-Papilionoideae into taxa of lower rank was subject for major discrepancies between traditional classifications while more recent phylogenetic studies provided no decisive answer to this problem. As a contribution towards resolving this situation, 81 morphological characters were recorded comparatively for 226 species and infra-specific taxa belonging to 75 genera representing 21 of the 32 tribes currently recognized in this subfamily. The data matrix was subjected to cluster analysis using the Sørensen distance measure and Ward’s clustering method of the PC-ord version-5 package of programs for Windows. This combination was selected from among the 56 combinations available in this package because it produced the taxonomically most feasible arrangement of the genera and species. The 75 genera are divided into two main groups A and B, whose recognition requires little more than the re-alignment of a few genera to resemble tribes 1-18 (Sophoreae to Hedysareae) and tribes 19-32 (Loteae to Genisteae), respectively, in the currently accepted classification. Only six of the 21 tribes represented by two or more genera seem sufficiently robust as the genera representing each of them hold together in only one of the two major groups A and B. Of the 29 genera represented by more than one species each 17, 7 and 5 are taxonomically coherent, nearly coherent and incoherent, respectively. The currently accepted circumscription and inter-relationships among the disrupted tribes and genera are in need of much detailed investigation.

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 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

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 Prototype-based Models for the Supervised Learning of Classification Schemes

2016 ◽  
Vol 12 (S325) ◽  
pp. 129-138
Author(s):  
Michael Biehl ◽  
Barbara Hammer ◽  
Thomas Villmann
Keyword(s):  
Vector Quantization ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
High Dimensional ◽  
Main Concept ◽  
Classification Schemes ◽  
Relevance Learning

AbstractAn introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

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