Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Classifying FM organisations using pattern recognition
–
To demonstrate the unintended negative results of the current rationale for classifying client FM organisations and suggest a classification system that can systematically group FM organisations.
Develops a classification model consisting of ten patterns built on the suggestions from the previous empirical studies for client FM organisations. It then applies these patterns onto 22 in-house FM organisations in the UK using the pattern recognition's unsupervised clustering for measuring the similarities in the sample population. This results in a detailed examination of the applicability and the validity of the classification system.
Three classes were found, two of which (Class 1 and 3) include mixed market sectors, while the other involves only healthcare FM organisations. The features of these classes are explained and the further use of the classification system is demonstrated and discussed.
The sample population including 22 client FM organisations is not an exhaustive list that can represent all FM organisations in general.
The suggested classification system adds value to the current market sector based classification by introducing ten patterns of FMO, used for measuring the similarities and dissimilarities of FM organisations.
Classifying FM organisations using pattern recognition
–
To demonstrate the unintended negative results of the current rationale for classifying client FM organisations and suggest a classification system that can systematically group FM organisations.
Develops a classification model consisting of ten patterns built on the suggestions from the previous empirical studies for client FM organisations. It then applies these patterns onto 22 in-house FM organisations in the UK using the pattern recognition's unsupervised clustering for measuring the similarities in the sample population. This results in a detailed examination of the applicability and the validity of the classification system.
Three classes were found, two of which (Class 1 and 3) include mixed market sectors, while the other involves only healthcare FM organisations. The features of these classes are explained and the further use of the classification system is demonstrated and discussed.
The sample population including 22 client FM organisations is not an exhaustive list that can represent all FM organisations in general.
The suggested classification system adds value to the current market sector based classification by introducing ten patterns of FMO, used for measuring the similarities and dissimilarities of FM organisations.
Classifying FM organisations using pattern recognition
Kaya, Sezgin (Autor:in) / Alexander, Keith (Autor:in)
Facilities ; 23 ; 570-584
01.11.2005
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Classifying FM organisations using pattern recognition
| Online Contents | 2005
Classifying client side FM organisations in the United Kingdom
| Emerald Group Publishing | 2006
Classifying client side FM organisations in the United Kingdom – 2
| Emerald Group Publishing | 2006
An inexpensive system for classifying tool wear states using pattern recognition
| British Library Online Contents | 1993
UB Braunschweig | 1868