Survey of state-of-the-art mixed data clustering algorithms
This is a survey paper, so it is incremental, summarizing existing research for practitioners and researchers in fields like health, finance, and marketing.
The paper tackles the problem of clustering mixed data (numeric and categorical features) by presenting a taxonomy and state-of-the-art review of algorithms, analyzing their strengths and weaknesses without introducing new methods or results.
Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to 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 or averaging, to 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. We then present a state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses 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.