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Can k-means handle categorical data?
The k-Means algorithm is not applicable to categorical data, as categorical variables are discrete and do not have any natural origin. So computing euclidean distance for such as space is not meaningful.
When to not use k-means?
k-means assume the variance of the distribution of each attribute (variable) is spherical; all variables have the same variance; the prior probability for all k clusters are the same, i.e. each cluster has roughly equal number of observations; If any one of these 3 assumptions is violated, then k-means will fail.
Can DBSCAN handle categorical variables?
Standard clustering algorithms like k-means and DBSCAN don’t work with categorical data. After doing some research, I found that there wasn’t really a standard approach to the problem.
Is the k means algorithm applicable to categorical data?
The standard k-means algorithm isn’t directly applicable to categorical data, for all kinds of reasons. The sample space for categorical data is discrete, and doesn’t have a natural origin. A Euclidean, or Manhattan, distance function on such a space isn’t really meaningful.
How does the K prototype calculate the distance between instances?
K-prototypes computes the distance between instances by combining the Euclidean distance between the numerical features and the hamming distance between the categorical features. You can’t really use k means with categorical/nominal data because it’s not ranked.
What’s the difference between k means and k-medoids?
This would make sense because a teenager is “closer” to being a kid than an adult is. A more generic approach to K-Means is K-Medoids. K-Medoids works similarly as K-Means, but the main difference is that the centroid for each cluster is defined as the point that reduces the within-cluster sum of distances.
Which is the best clustering algorithm for categorical data?
In my opinion, there are solutions to deal with categorical data in clustering. R comes with a specific distance for categorical data. This distance is called Gower and it works pretty well. The choice of k-modes is definitely the way to go for stability of the clustering algorithm used.