Fuzzy Cluster Analysis of Partially Missing Data Sets
| Type of publication: | Article |
| Citation: | timm2002fuzzyclusteranalysis |
| Journal: | Second International Workshop on Hybrid Methods for Adaptive Systems I |
| Year: | 2002 |
| Pages: | 426--431 |
| Abstract: | A common problem in data analysis are missing attribute values in datasets. The easiest way to handle such datasets in fuzzy cluster analysis is to discard data with missing values. Since this complete case approach may result in a loss of valuable information and reduced dataset size, we study how missing values can be handled by modified fuzzy clustering methods. These approaches are based on iterated imputation of missing values, available case estimation of the cluster parameters, and the introduction of a class specific probability for missing values. Benchmark datasets with randomly deleted attribute values are used to demonstrate the capability of the presented approaches. Our experiments show that the modified clustering methods are superior to a complete case analysis. |
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| Authors | |
| Added by: | [GR] |
| Total mark: | 0 |
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