Importance–Performance Analysis by Fuzzy C-Means Algorithm
•We propose a method of fuzzy clustering in Importance–Performance Analysis (IPA).•The fuzzy partition of a set of attributes is obtained by Fuzzy C-Means Algorithm.•The results are more suitable for deriving managerial decisions than by the traditional IPA.•We exemplify and compare the results with...
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| Published in | Expert systems with applications Vol. 50; pp. 9 - 16 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
15.05.2016
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2015.12.023 |
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| Abstract | •We propose a method of fuzzy clustering in Importance–Performance Analysis (IPA).•The fuzzy partition of a set of attributes is obtained by Fuzzy C-Means Algorithm.•The results are more suitable for deriving managerial decisions than by the traditional IPA.•We exemplify and compare the results with those obtained by the traditional IPA.
Traditional Importance–Performance Analysis assumes the distribution of a given set of attributes in four sets, “Keep up the good work”, “Concentrate here”, “Low priority” and “Possible overkill”, corresponding to the four possibilities, high–high, low–high, low–low and high–low, of the pair performance–importance. This can lead to ambiguities, contradictions or non-intuitive results, especially because the most real-world classes are fuzzy rather than crisp. The fuzzy clustering is an important tool to identify the structure in data, therefore we apply the Fuzzy C-Means Algorithm to obtain a fuzzy partition of a set of attributes. A membership degree of every attribute to each of the sets mentioned above is determined, against to the forcing categorization in traditional Importance–Performance Analysis. The main benefit is related with the deriving of the managerial decisions which become more refined due to the fuzzy approach. In addition, the development priorities and the directions in which the effort of an economic or non-economic entity would be useless or even dangerous are identified on a rigorous basis and taking into account only the internal structure of the input data. |
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| AbstractList | •We propose a method of fuzzy clustering in Importance–Performance Analysis (IPA).•The fuzzy partition of a set of attributes is obtained by Fuzzy C-Means Algorithm.•The results are more suitable for deriving managerial decisions than by the traditional IPA.•We exemplify and compare the results with those obtained by the traditional IPA.
Traditional Importance–Performance Analysis assumes the distribution of a given set of attributes in four sets, “Keep up the good work”, “Concentrate here”, “Low priority” and “Possible overkill”, corresponding to the four possibilities, high–high, low–high, low–low and high–low, of the pair performance–importance. This can lead to ambiguities, contradictions or non-intuitive results, especially because the most real-world classes are fuzzy rather than crisp. The fuzzy clustering is an important tool to identify the structure in data, therefore we apply the Fuzzy C-Means Algorithm to obtain a fuzzy partition of a set of attributes. A membership degree of every attribute to each of the sets mentioned above is determined, against to the forcing categorization in traditional Importance–Performance Analysis. The main benefit is related with the deriving of the managerial decisions which become more refined due to the fuzzy approach. In addition, the development priorities and the directions in which the effort of an economic or non-economic entity would be useless or even dangerous are identified on a rigorous basis and taking into account only the internal structure of the input data. Traditional Importance-Performance Analysis assumes the distribution of a given set of attributes in four sets, "Keep up the good work", "Concentrate here", "Low priority" and "Possible overkill", corresponding to the four possibilities, high-high, low-high, low-low and high-low, of the pair performance-importance. This can lead to ambiguities, contradictions or non-intuitive results, especially because the most real-world classes are fuzzy rather than crisp. The fuzzy clustering is an important tool to identify the structure in data, therefore we apply the Fuzzy C-Means Algorithm to obtain a fuzzy partition of a set of attributes. A membership degree of every attribute to each of the sets mentioned above is determined, against to the forcing categorization in traditional Importance-Performance Analysis. The main benefit is related with the deriving of the managerial decisions which become more refined due to the fuzzy approach. In addition, the development priorities and the directions in which the effort of an economic or non-economic entity would be useless or even dangerous are identified on a rigorous basis and taking into account only the internal structure of the input data. |
| Author | Ban, Adrian I. Ban, Olimpia I. Tuşe, Delia A. |
| Author_xml | – sequence: 1 givenname: Olimpia I. surname: Ban fullname: Ban, Olimpia I. email: oban@uoradea.ro organization: Department of Economics, University of Oradea, Universităţii 1, Oradea 410087, Romania – sequence: 2 givenname: Adrian I. surname: Ban fullname: Ban, Adrian I. email: aiban@uoradea.ro organization: Department of Mathematics and Informatics, University of Oradea, Universităţii 1, Oradea 410087, Romania – sequence: 3 givenname: Delia A. surname: Tuşe fullname: Tuşe, Delia A. email: dmerca@uoradea.ro organization: Department of Mathematics and Informatics, University of Oradea, Universităţii 1, Oradea 410087, Romania |
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| SubjectTerms | Algorithms Crisps Dangerous Economics Expert systems Fuzzy Fuzzy C-Means Algorithm Fuzzy clustering Fuzzy partition Fuzzy sets Importance–Performance Analysis Priorities Prototype |
| Title | Importance–Performance Analysis by Fuzzy C-Means Algorithm |
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