I am trying to cluster a few categorical & continuous observations using an unsupervised cluster algorithm. I have used two-step clustering since I have categorical predictors! Could someone help me with algorithms to compute predictor importance in unsupervised learning?
Related to your query, I suggest you follow https://machinelearningmastery.com/feature-selection-with-categorical-data/
Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space.
Types of Clustering
Hard Clustering: In hard clustering, each data point either belongs to a cluster completely or not. ...
Soft Clustering: In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.
Chapter Unsupervised Learning Methods
Muhammad Ali Thank you for your response. I couldn't find a link on unsupervised clustering, though!
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