Importance–Performance Analysis by Fuzzy C-Means Algorithm |
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| Affiliation: | 1. Department of Economics, University of Oradea, Universit??ii 1, Oradea 410087, Romania;2. Department of Mathematics and Informatics, University of Oradea, Universit??ii 1, Oradea 410087, Romania;1. Department of Aerospace Engineering, University of Michigan, Ann Arbor, Michigan 48109, United States;2. Department of Pediatrics-Division of Pediatric Endocrinology, University of Michigan Health System, Ann Arbor, Michigan 48109, United States;3. Department of Radiology, Section of Pediatric Radiology University of Michigan Health System, Ann Arbor, Michigan 48109, United States;1. Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata 700015, India;2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India;3. Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India;1. Grado Department of Industrial and Systems Engineering, System Performance Laboratory, Virginia Tech, Falls Church, VA 22043, USA;2. Business Analytics & Statistics Department, Haslam College of Business, The University of Tennessee, Knoxville, TN 37996-0532, USA;3. Centre for Management of Technology and Entrepreneurship, University of Toronto, 200 College Street, Toronto, Ontario M5S 3E5, Canada;4. Rogers Communications Inc., Toronto, Ontario M4W 1G9, Canada;1. Institute for Technological Development and Innovation in Communications, Spain;2. Signal and Communications Department, Spain;3. Telematic Engineering Department, University of Las Palmas de Gran Canaria, Campus Universitario de Tafira S/N, 35017 Las Palmas de Gran Canaria, Spain;1. Department of Computer science and Mathematics, Lebanese American University, Beirut, Lebanon;2. Department of Electrical & Computer Engineering, Khalifa University of Science, Technology & Research, Abu Dhabi, UAE;3. Concordia Institute for Information Systems Engineering, Montreal, Canada |
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| Abstract: | 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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