الفهرس 
Title : A goal programming approach to fuzzy cluster analysis with missing data
AbstractOnly 14 pages are availabe for public view
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Abstract
Cluster analysis plays an important role in a wide spectrum of applications. In practice, some problems such as handling missing data, noise or outliers are often faced. So, the clustering problem becomes more challenging in the presence of such problems. This thesis addresses the problem of fuzzy cluster analysis with missing data as follows: Firstly, it presents methods of fuzzy cluster analysis-which were discussed in the literature-that can be extended to process datasets with missing values through incorporating them in the data analysis process rather than pre-processing. Secondly, it proposes a new approach for treating the problem of missing data in fuzzy cluster analysis based on goal programming