Nowadays, the application of data mining in the healthcare industry is necessary. Data mining brings a set of tools and techniques that can be applied to discover hidden patterns that provide healthcare professionals an additional source of knowledge for making decisions. In more detail, clustering the patients that have the same status helps discovering new disease, but the suitable number of clusters is not often obvious. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Then, an improved algorithm is presented for learning k while clustering. Finally, we evaluate the algorithm, apply to dataset of patients and results show its efficiency.
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