Clustering Human Wrist Pulse Signals via Multiple Criteria Decision Making

In this paper, we cluster a unlabeled human wrist pulse signal data set via a multiple criteria decision making (MCDM) framework to mine useful information for further study. First, a preprocessing scheme is performed and spatial features are extracted to represent a pulse signal. Then, a list of clustering algorithms are initialized to generate a number of clustering alternatives. The goodness of these clustering alternatives are sequentially comprehensively evaluated by 11 criteria, including ten internal cluster validation indices and an ad-hoc index, the robustness to noise, which is proposed for assessing the clustering alternatives of the pulse data set with spatial features. Taking the evaluation results as inputs, the technique for order preference by similarity to ideal solution (TOPSIS) method is employed to solve the resulting MCDM model. According to the TOPSIS rank, clustering the data set into thirteen clusters via k-means is optimal. Samples drawn from each cluster have similar patterns, corresponding to specific pulse type in traditional Chinese pulse diagnosis. The thirteen clusters are segregated into two groups, namely the healthy and the unhealthy, which can be further applied for unhealthy pulse detection.