The Golf Players' Motivations: The Algarve Case

2006 ◽  
Vol 6 (3) ◽  
pp. 227-238 ◽  
Author(s):  
Antonia Correia ◽  
Pedro Pintassilgo
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Social Environment ◽  
Principal Components ◽  
Golf Courses ◽  
The Sun ◽  
Market Segments ◽  
The Third ◽  
Components Analysis ◽  
Selection Of

The purpose of this article is to investigate the motivations behind golf demand in the Algarve — one of Europe's most popular golf destinations. The research is based on the results of a survey on the golf demand of Algarve's golf courses, held in 2002. In order to identify the main motives behind golf demand in the region, a principal components analysis was performed. Four main choice factors were identified to explain the selection of Algarve's golf courses. The first was designated social environment and is associated with motives such as events and beaches. The second, leisure, is related to restaurants and bars, landscape, weather and accommodation. The third, entitled golf, is directly related to characteristics of courses. The fourth, logistics, is associated with variables such as price and accessibility. It is also found, through a cluster analysis that the choice factors can be associated with three market segments: the tourist golfer, who is mostly concerned with the golf courses and the game; the householder golfer, essentially centred on accommodation, gastronomy, landscape, weather, price and accessibility; and finally, the sun-beach tourist, who is mostly interested in tourist opportunities.

Multivariate Analysis of Remains of Molluscan Foods Consumed by Latest Pleistocene and Holocene Humans in Nerja Cave, Málaga, Spain

1997 ◽  
Vol 48 (2) ◽  
pp. 215-227 ◽  
Author(s):  
Francisco Serrano ◽  
Antonio Guerra-Merchán ◽  
Carmen Lozano-Francisco ◽  
José Luis Vera-Peláez
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Late Quaternary ◽  
Western Mediterranean ◽  
Temperature Curve ◽  
Sea Surface ◽  
The Third ◽  
Holocene Sediments ◽  
Components Analysis

AbstractNerja Cave is a karstic cavity used by humans from Late Paleolithic to post-Chalcolithic times. Remains of molluscan foods in the uppermost Pleistocene and Holocene sediments were studied with cluster analysis and principal components analysis, in bothQ and R modes. The results from cluster analysis distinguished interval groups mainly in accordance with chronology and distinguished assemblages of species mainly according to habitat. Significant changes in the shellfish diet through time were revealed. In the Late Magdalenian, most molluscs consumed consisted of pulmonate gastropods and species from sandy sea bottoms. The Epipaleolithic diet was more varied and included species from rocky shorelines. From the Neolithic onward most molluscs consumed were from rocky shorelines. From the principal components analysis inQ mode, the first factor reflected mainly changes in the predominant capture environment, probably because of major paleogeographic changes. The second factor may reflect selective capture along rocky coastlines during certain times. The third factor correlated well with the sea-surface temperature curve in the western Mediterranean (Alboran Sea) during the late Quaternary.

Chemistry research in Europe: A publication analysis (2006–2016)

2019 ◽  
pp. 016555151986549
Author(s):  
Hakan Kaygusuz
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Polymer Science ◽  
Chemistry Research ◽  
Research Outcome ◽  
Components Analysis ◽  
Number Of Publications

In this article, chemistry research in 51 different European countries between years 2006 and 2016 was studied using statistical methods. This study consists of two parts: In the first part, different economical, institutional and citation parameters were correlated with the number of publications, citations and chemical industry numbers using principal components analysis and hierarchical cluster analysis. The results of the first part indicated that economical and geographical parameters directly affect the chemistry research outcome. In the second part, research in branches of chemistry and related disciplines such as analytical chemistry, polymer science and physical chemistry were analysed using principal components analysis and hierarchical cluster analysis for each country. Publication data were collected as the number of chemistry publications (in Science Citation Index–Expanded (SCI-E)) between years 2006 and 2016 in different chemistry subdisciplines and related scientific areas. Results of the second part of the study produced geographical and economical clusters of countries, interestingly, without addition of any geographical data.

scholarly journals A method of dimensionality reduction by selection of components in principal component analysis for text classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1499-1506 ◽  
Author(s):  
Yangwu Zhang ◽  
Guohe Li ◽  
Heng Zong
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Principal Components Analysis ◽  
Text Classification ◽  
Principal Components ◽  
Principal Component ◽  
Method Of Dimensionality Reduction ◽  
Components Analysis ◽  
Selection Of

Dimensionality reduction, including feature extraction and selection, is one of the key points for text classification. In this paper, we propose a mixed method of dimensionality reduction constructed by principal components analysis and the selection of components. Principal components analysis is a method of feature extraction. Not all of the components in principal component analysis contribute to classification, because PCA objective is not a form of discriminant analysis (see, e.g. Jolliffe, 2002). In this context, we present a function of components selection, which returns the useful components for classification by the indicators of the performances on the different subsets of the components. Compared to traditional methods of feature selection, SVM classifiers trained on selected components show improved classification performance and a reduction in computational overhead.

Ranking e valutazione: il caso delle classifiche delle universitŕ

2009 ◽  
pp. 81-114
Author(s):  
Ferruccio Biolcati Rinaldi ◽  
Daniele Checchi ◽  
Chiara Guglielmetti ◽  
Silvia Salini ◽  
Matteo Turri
Keyword(s):  
Cluster Analysis ◽  
Key Words ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Partial Replication ◽  
Validity And Reliability ◽  
University Ranking ◽  
International Ranking ◽  
Components Analysis ◽  
And Cluster Analysis

- Abstract The paper consists of two parts. The first is more general: it introduces to university ranking, shows the leading international ranking, discusses the uses people make of rankings. The second focuses on Italian ranking Censis-la Repubblica developing two different kinds of analyses: after considering indicators validity and reliability, principal components analysis and cluster analysis are applied to a partial replication of Censis-la Repubblica data. A list of points to pay attention comes out of these analyses: it can be useful when defining rankings of complex institutions such as universities.Key words: ranking, university ranking, Censis-la Repubblica, validity and reliability, normalisation and combination of indicators.

scholarly journals Morphological Differentiation of the Genus Niphargus (Amphipoda) on Corsica — A Numerical Approach

1984 ◽  
Vol 54 (1) ◽  
pp. 147-155
Author(s):  
W. Hovenkamp ◽  
F. Hovenkamp ◽  
J.J. van der Heide
Keyword(s):  
Cluster Analysis ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Morphological Differentiation ◽  
Taxonomic Status ◽  
Numerical Approach ◽  
Short Introduction ◽  
Size Dependent ◽  
Methods Of Analysis ◽  
Components Analysis

A short introduction is provided on the taxonomic status of the genus Niphargus, especially on the species related to N. longicaudatus corsicanus. Previous findings and descriptions are mentioned. An attempt is made to clarify the relationships between Corsican Niphargus populations by means of a cluster analysis and a principal components analysis combined with a cluster analysis. Special attention has been paid to the size-dependent variability of most of the characters. The results of both methods of analysis are compared with each other and evaluated. The morphological differentiation between populations is, on the average, greater than within populations. This, along with the large amount of character variability, makes it very difficult to fit populations into, or to distinguish them from, any of the — often poorly described — taxa of Niphargus.

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