How does k-means clustering for mixed numeric and categorical data?
It uses a distance measure which mixes the Hamming distance for categorical features and the Euclidean distance for numeric features. A Google search for “k-means mix of categorical data” turns up quite a few more recent papers on various algorithms for k-means-like clustering with a mix of categorical and numeric data.
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.
Which is an example of association between categorical variables?
If statistical assumptions are met, these may be followed up by a chi-square test. As an example, we’ll see whether sector_2010 and sector_2011 in freelancers.sav are associated in any way. Before doing anything else, let’s first just take a quick look at both variables separately.
When is clustering of observations not really worth doing?
If the clustering of observations does necessarily entail relationships between the variables and vice versa, does that imply that clustering is not really worth doing when you only have categorical data (i.e., should you just analyze the variables instead)?
How to use k-means for mixed data?
K-means uses Euclidean distance, which is not defined for categorical data. Therefore, to use K-means type or partitional clustering algorithm on mixed data you have to change the cost function s.t. it can capture distance or similarity between both the types of data.
How is the K prototype used in clustering?
We are using the Elbow method to determine the optimal number of clusters for K-Prototype clusters. Instead of calculating the within the sum of squares errors (WSSE) with Euclidian distance, K-Prototype provides the cost function that combines the calculation for numerical and categorical variables.
Which is the best algorithm for clustering mixed data?
K-Prototype is a clustering method based on partitioning. Its algorithm is an improvement of the K-Means and K-Mode clustering algorithm to handle clustering with the mixed data types.