Abstract
Mixed data comprises of both numeric and categorical features, and they frequently occur in various domains, such as health, finance, and marketing. Clustering is often sought on mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging because it is difficult to directly apply mathematical operations, such as summation, averaging, on the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. Then, we present a state-of-the-art review of the research works within each research theme. We analyze the strengths and weakness of these methods with pointers for future research directions. Lastly, we present an in-depth analysis of the overall challenges in this field, highlight open research questions and discuss guidelines to make progress in the field.
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URL
http://arxiv.org/abs/1811.04364