My Cookie Mix

The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and deg

"Organizations that allocate the right resources, influence, and authority to the CDO are four times more likely to be successful using data to transform the business, according to Laney’s research. By comparison, organizations turning to the CIO to oversee data management and strategy are only half as likely to see business impact from advanced analytics initiatives." Here's why:  https://mitsloan.mit.edu/ideas-made-to-matter/making-business-case-a-chief-data-officer