K-Prototype clustering is an algorithm to combine K-Means and K-Modes. K-Prototype can handle both continuous and categorical data to create clusters.
For K-Prototype Python Lab we will be using RFMTC marketing model (a modified version of RFM). The data contains 748 donor data, each one included R (Recency – months since last donation), F (Frequency – total number of donation), M (Monetary – total blood donated in c.c.), T (Time – months since first donation), and a binary variable representing whether he/she donated blood in March 2007 (1 stand for donating blood; 0 stands for not donating blood).
Download the Python code used in the session from below:
Let’s listen to Prof.Dinesh to understand K-Prototype in Python.
K-Prototype clustering uses “Huang” and “cao” initialisation, you may read more about this in the given document.