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Can we use clustering technique for categorical variables?
It is simply not possible to use the k-means clustering over categorical data because you need a distance between elements and that is not clear with categorical data as it is with the numerical part of your data.
What is the proper machine learning algorithm to create customer segmentation?
K-means clustering is a popular unsupervised machine learning algorithm method. In layman terms, it finds all of the different “clusters” and groups them together while keeping them as small as possible. That means that you end up with the most possible customer segments to interpret.
How are continuous and categorical segmentation variables handled?
The algorithm is able to handle both continuous and categorical segmentation variables. Latent class cluster analysis uses probability modeling to maximize the overall fit of the model to the data.
Why are datasets having both numerical and categorical variables?
Clustering is nothing but segmentation of entities, and it allows us to understand the distinct subgroups within a data set. While many articles review the clustering algorithms using data having simple continuous variables, clustering data having both numerical and categorical variables is often the case in real-life problems.
How are segmentation approaches used in decision analysis?
The model can identify patterns in multiple dependent variables (such as attitudes and needs) and quantify correlation of dependent variables with related variables (such as buying behaviors). For each survey respondent, the analysis delivers the probability of belonging to each cluster (segment).
Is it possible to cluster categorical data into integers?
I don’t really see a reason why simple K-Means clustering shouldn’t work. If you convert your categorical data into integers (or encode to binary where one column is equal to one category, so called “one-hot encoding”), you can then fetch it into the algorithm.