Are We Really That Close Together? Tracing and Discussing Similarities and Differences between Greek Terrorist Groups Using Cluster Analysis

2017 ◽  
Author(s):  
Ioanna Lekea ◽  
Panagiotis Karampelas
Keyword(s):  
Cluster Analysis ◽  
Terrorist Groups ◽  
Similarities And Differences

scholarly journals Similarities and Differences in Operating the Performance of Audit Firms in Serbia

2015 ◽  
Vol 53 (4) ◽  
pp. 535-545
Author(s):  
Dejan Jakšić ◽  
Kristina Mijić ◽  
Ljiljana Bonić
Keyword(s):  
Cluster Analysis ◽  
Performance Indicators ◽  
Human Potential ◽  
Net Income ◽  
Market Position ◽  
Audit Firms ◽  
Similarities And Differences ◽  
Big Four

Abstract Cluster analysis of audit firms in Serbia was carried out in order to assess the similarities and differences between audit firms. This analysis shows that “Big four” audit firms are significantly different from other audit firms by market position and human potential, but not according to net income. In addition, it can be noted that there are significant differences in observed performance indicators between individual audit firms.

A Morphometric Approach in South American Stipoids with Plumose and Pappus-like Awns

2020 ◽  
Vol 45 (2) ◽  
pp. 233-241
Author(s):  
Sergio E. Sclovich ◽  
Agostina B. Sassone ◽  
Silvana M. Sede ◽  
Liliana M. Giussani
Keyword(s):  
Cluster Analysis ◽  
Generalized Linear Models ◽  
Linear Models ◽  
Univariate Analysis ◽  
Principal Coordinate Analysis ◽  
South American ◽  
Closely Related Species ◽  
Similarities And Differences ◽  
And Cluster Analysis ◽  
Univariate Analyses

Abstract—Within grasses, the tribe Stipeae is highly specialized by the presence of only one fruit per spikelet and characterized by the diversity in the ornamentation of the floret. Our aim was to analyze similarities and differences based on multi- and univariate analyses among closely related species in American Stipeae: Jarava species with plumose-like awns and species of Pappostipa with pappus-like awns. Ordination analyses (principal coordinate analysis and cluster analysis) were used to determine major groupings, while significant differences among groups were tested by multivariate analysis of variance (MANOVA) and univariate analysis based on generalized linear models (GLM). Based on morphological similarities, we delimited five groups. The presence of a small floret characterized Jarava annua, J. media, and J. plumosula, although J. annua was clearly distinguished by the distribution of hairs in the awn column. Jarava subplumosa and J. psylantha were characterized by the pubescence of the culm, the length of the floret callus, the length of the awn subule, and the length of the awn hairs. Pappostipa was distinctive by having hairs only in the awn column that resemble a pappus while Jarava neaei + J. pogonathera presented the longest inflorescences and hairs only on the awn subule, resembling a feather. As a result, we present a key to taxa and descriptions to characterize and identify species within the Jarava-Pappostipa group with plumose and pappus-like awns.

Identifying Bands in the Knowledge Exchange Spectrum in an Online Health Infomediary

2015 ◽  
Vol 10 (3) ◽  
pp. 63-84 ◽  
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This study proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

Behavioural models for manufacturing firms: analysing survey data

2011 ◽  
pp. 139-163
Author(s):  
Luciana Crosilla ◽  
Marco Malgarini
Keyword(s):  
Cluster Analysis ◽  
Survey Data ◽  
Manufacturing Firms ◽  
Economic Situation ◽  
Subsequent Recovery ◽  
Survey Questions ◽  
Depth Study ◽  
Survey Results ◽  
Similarities And Differences ◽  
Simple Average

Survey data on manufacturing firms are usually analysed on an aggregate basis, calculating for each question the so-called "balance" between the number of positive and negative replies. The simple average of selected balances is then commonly used to calculate the confidence indicator. While balance and confidence indicators provide an easy-to-compute and easy-to-understand quantification of survey results, and therefore are considered useful tools to analyse the sector's cyclical situation, a cyclical analysis based on balance and confidence indicators alone fails to fully exploit all the information embedded in the survey. More specifically, computation of the balance statistic disregards "neutral" answers to survey questions and no attempt is made to identify potential relationships between the different responses to the various survey questions given by the same firms. A more in-depth study of this information can provide interesting insights into firms' opinions on the economic situation. The contribution presents a new methodology based on cluster analysis that takes into account also the neutral answers and then uses it to assess the similarities and differences between the recent crisis and current recovery, and to compare these to past cyclical crises, specifically, the major recession of 1992-1993 and subsequent recovery in 1993-1995.

User Behaviors and Knowledge Exchange in Health Infomediary

2018 ◽  
pp. 213-233
Author(s):  
Dobin Yim ◽  
Jiban Khuntia ◽  
Young Anna Argyris
Keyword(s):  
Cluster Analysis ◽  
Cosmetic Surgery ◽  
Knowledge Exchange ◽  
Large Data ◽  
Two Dimensions ◽  
Data Set ◽  
User Behaviors ◽  
Large Data Set ◽  
Similarities And Differences

Online health infomediaries have the objective of knowledge exchange between participants. Visitor contribution is an important factor for the success of the infomediaries. Providers engaged with infomediaries need visitor identification for reputational incentives. However, identification or classification of visitors in online health infomediaries is sparse in literature. This chapter proposes two dimensions of participation, the intention and intensity levels of visitors, to conceptualize four user categories: community supporters, experiencer providers, knowledge questors, and expertise contributors. The authors validate these categories using a unique large data set collected from a health infomediary for cosmetic surgery, and consisting of 162,598 observed activities of 44,350 visitors, at different participation levels in the year 2012-13. They use cluster analysis to describe similarities and differences among the four user categories. Practice implications are discussed.

scholarly journals Patterns in clinical chemistry requests

1989 ◽  
Vol 11 (2) ◽  
pp. 55-63
Author(s):  
Jan B. Hemel ◽  
Frans R. Hindriks ◽  
Hilko van der Voet ◽  
Leo R. Rijnveld
Keyword(s):  
Internal Medicine ◽  
Cluster Analysis ◽  
Multidimensional Scaling ◽  
Clinical Chemistry ◽  
Statistical Significance ◽  
Chemistry Laboratory ◽  
Hospital Department ◽  
Clinical Chemistry Laboratory ◽  
Similarities And Differences ◽  
Test Profiles

For each patient sample that is presented to the clinical chemistry laboratory a combination of various tests can be requested. This combination or profile will depend on the condition of the patient, and hence also on the requesting hospital department. Several techniques were applied to detect and describe patterns in tests requested by the cardiology, hepatology and nephrology sections of the out-patient's Department for Internal Medicine. Comparison of the frequencies of ordering the tests showed significant differences between these sections. Cluster analysis and multidimensional scaling were used to show similarities and differences in the test profiles that were used by the sections. These techniques are useful for generating hypotheses, but the statistical significance of the clustering found is difficult to assess.

Classifying healthcare warehouses according to their performance. A Cluster Analysis-based approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Anna Corinna Cagliano ◽  
Giulio Mangano ◽  
Carlo Rafele ◽  
Sabrina Grimaldi
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Design Methodology ◽  
Organizational Characteristics ◽  
Statistical Tool ◽  
Chain Management ◽  
Content Type ◽  
Similarities And Differences ◽  
The Empirical Analysis ◽  
Practical Implications

PurposeThe objective of this paper is to propose an approach to comparatively analyze the performance of drugs and consumable products warehouses belonging to different healthcare institutions.Design/methodology/approachA Cluster Analysis is completed in order to classify warehouses and identify common patterns based on similar organizational characteristics. The variables taken into account are associated with inventory levels, the number of SKUs, and incoming and outgoing flows.FindingsThe outcomes of the empirical analysis are confirmed by additional indicators reflecting the demand level and the associated logistics flows faced by the warehouses at issue. Also, the warehouses belonging to the same cluster show similar behaviors for all the indicators considered, meaning that the performed Cluster Analysis can be considered as coherent.Research limitations/implicationsThe study proposes an approach aimed at grouping healthcare warehouses based on relevant logistics aspects. Thus, it can foster the application of statistical analysis in the healthcare Supply Chain Management. The present work is associated with only one regional healthcare system.Practical implicationsThe approach might support healthcare agencies in comparing the performance of their warehouses more accurately. Consequently, it could facilitate comprehensive investigations of the managerial similarities and differences that could be a first step toward warehouse aggregation in homogeneous logistics units.Originality/valueThis analysis puts forward an approach based on a consolidated statistical tool, to assess the logistics performances in a set of warehouses and, in turn to deepen the related understanding as well as the factors determining them.

scholarly journals Revisiting the ENL-ESL-EFL continuum: A multifactorial approach to grammatical aspect in spoken Englishes

ICAME Journal ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 41-78 ◽  
Author(s):  
Paula Rautionaho ◽  
Sandra C. Deshors ◽  
Lea Meriläinen
Keyword(s):  
Cluster Analysis ◽  
Statistical Technique ◽  
Grammatical Aspect ◽  
Semantic Domain ◽  
Cross Variety ◽  
Similarities And Differences ◽  
Uniform Manner ◽  
Multifactorial Approach ◽  
Verb Tense ◽  
The Continuum

AbstractThis study focuses on the progressive vs. non-progressive alternation to revisit the debate on the ENL-ESL-EFL continuum (i.e. whether native (ENL) and nonnative (ESL/EFL) Englishes are dichotomous types of English or form a gradient continuum). While progressive marking is traditionally studied independently of its unmarked counterpart, we examine (i) how the grammatical contexts of both constructions systematically affect speakers’ constructional choices in ENL (American, British), ESL (Indian, Nigerian and Singaporean) and EFL (Finnish, French and Polish learner Englishes) and (ii) what light speakers’ varying constructional choices bring to the continuum debate. Methodologically, we use a clustering technique to group together individual varieties of English (i.e. to identify similarities and differences between those varieties) based on linguistic contextual features such as AKTIONSART, ANIMACY, SEMANTIC DOMAIN (of aspect-bearing lexical verb), TENSE, MODALITY and VOICE to assess the validity of the ENL-ESL-EFL classification for our data. Then, we conduct a logistic regression analysis (based on lemmas observed in both progressive and non-progressive constructions) to explore how grammatical contexts influence speakers’ constructional choices differently across English types. While, overall, our cluster analysis supports the ENL-ESL-EFL classification as a useful theoretical framework to explore cross-variety variation, the regression shows that, when we start digging into the specific linguistic contexts of (non-)progressive constructions, this classification does not systematically transpire in the data in a uniform manner. Ultimately, by including more than one statistical technique into their exploration of the continuum, scholars could avoid potential methodological biases.

scholarly journals SIMILARITIES AND DIFFERENCES IN THE CORE MARKETS FOR AGRICULTURAL PRODUCTS IN POLAND AFTER THE ACCESSION TO THE EUROPEAN UNION

2018 ◽  
Vol 47 (1) ◽  
pp. 13-19
Author(s):  
Andrzej Czyżewski ◽  
Dariusz Czakowski
Keyword(s):  
Cluster Analysis ◽  
Balance Sheet ◽  
Agricultural Products ◽  
Data Sources ◽  
The European Union ◽  
Domestic Consumption ◽  
Sugar Beets ◽  
The Core ◽  
Data Production ◽  
Similarities And Differences

This paper attempts to determine the similaritybetween individual markets for agricultural products, consideringthe evolution of basic balance sheet data (production,domestic consumption, imports and exports) and real purchaseprices of agricultural products. The main markets foragricultural products include those demonstrating the highestproduction volumes of commodity such as cereals, potatoes,sugar beets (sugar), rape, fruit, vegetables, pork, beef andpoultry, cow’s milk and eggs in the study period. Based onthe search for data sources, and considering the specifics ofempirical data collected, it was decided that the followingmethods will be used to pursue the objectives of this study:principal component analysis, cluster analysis based on theWard’s method and k-means. The research allowed to identifythree groups of Polish markets which followed a similardevelopment trend after Poland’s accession to the EuropeanUnion. The first group consisted of markets for cereals, rape,poultry and fruits. The second cluster included markets forcow’s milk, eggs and beef. The last one was composed of potato,sugar beet, vegetable and pork markets.

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

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