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A heuristic method for finding the optimal number of clusters with application In medical data

Bayati, H ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1109/iembs.2008.4650258
  3. Publisher: IEEE Computer Society , 2008
  4. Abstract:
  5. In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets. © 2008 IEEE
  6. Keywords:
  7. Fuzzy clustering ; Fuzzy inference ; Fuzzy systems ; K-means clustering ; Simulated annealing ; Cluster validity indices ; Davies-Bouldin index ; Fuzzy C mean ; Growing neural gas ; K-means ; Medical data ; Medical data sets ; Optimal number ; Heuristic methods ; Algorithm ; Artificial intelligence ; Automated pattern recognition ; Biological model ; Cluster analysis ; Computer simulation ; Fuzzy logic ; Methodology ; Statistical analysis ; Statistical model ; Theoretical model ; Algorithms ; Artificial Intelligence ; Cluster Analysis ; Computer Simulation ; Data Interpretation, Statistical ; Gene Expression Profiling ; Medical Informatics ; Models, Genetic ; Models, Statistical ; Models, Theoretical ; Pattern Recognition, Automated
  8. Source: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, 20 August 2008 through 25 August 2008 ; 2008 , Pages 4684-4687 ; 9781424418152 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4650258