Multi-objective semi-supervised clustering to identify health service patterns for injured patients.
Hadi A KhorshidiUwe AickelinGholamreza HaffariBehrooz Hassani-MahmooeiPublished in: Health information science and systems (2019)
The proposed multi-objective semi-supervised clustering finds the optimal clusters that not only are well-separated from each other but can provide informative insights regarding the outcome of interest. It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.