How to cluster mixed categorical and continuous data?

A short discussion of methods for clustering mixed datasets of categorical and continuous data. Recently I had to do some clustering of data that contained both continuous and categorical features. Standard clustering algorithms like k-means and DBSCAN don’t work with categorical data.

How to cluster mixed type data for machine learning?

In the second part, I’m illustrating the approach using a bank marketing dataset available on uci’s machine learning repository, and some useful functions from cluster and Rtsne packages.

How to encode categorical data before clustering?

Encode the categorical data before clustering Next we’ll try encoding the categorical data using one hot encoding so that we can include it in k-means clustering (note that you may also want to try scaling the data after OHE but I didn’t do that here for succinctness).

How to use FAMD for mixed categorical data?

Our final approach is to use FAMD (factor analysis for mixed data) to convert our mixed continuous and categorical data into derived continuous components (I chose 3 components here). I defer to the Prince documentation for an explanation of how the FAMD algorithm works.

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 to use agglomerative clustering for categorical data?

Agglomerative clustering will start with n clusters, where n is the number of observations, assuming that each of them is its own separate cluster. Then the algorithm will try to find most similar data points and group them, so they start forming clusters.

Can a k-means clustering algorithm work with mixed data?

However, both of these distance metrics are applicable only for continuous data. In our data which contains mixed data types, Euclidean and Manhattan distances are not applicable and therefore, algorithms such as K-means and hierarchical clustering would fail to work.

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 the best unsupervised learning algorithm for clustering?

K-means is one of the simplest unsupervised learning algorithms that solves the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters (assume k clusters) fixed a priori.

Which is the best approach to clustering data?

As always with data science, there is no one approach suited to all problems – in my opinion, clustering in particular is as much an art as a science.

How to cluster mixed data types in Python?

The first clustering method we will try is called K-Prototypes. This algorithm is essentially a cross between the K-means algorithm and the K-modes algorithm. To refresh our memory, K-means clusters data using euclidean distance. Meanwhile, K-modes clusters categorical data based off the number of matching categories between data points.

Which is a proposed approach to clustering using R?

A proposed approach using R | by Thomas Filaire | Towards Data Science Which methodology to group individuals based on their common patterns and similarities when expressed in multiple data types? Clustering unsupervised data is not an easy task.

Why is a categorical variable used in a model?

The decision to model a categorical variable as a set of fixed events or as a sample of possible events of some unobserved random variable determines what interpretations can be made from the model. If fixed effects are used, inferences can be made about the specific levels of the categorical variable as well as differences between levels.

Which is a variable included in a mixed model?

Y ∼ N ( X β, σ 2 I). A linear mixed model includes at least one unobserved variable. The unobserved variable is modelled in both the fixed and random parts of a mixed model. The mean of an unobserved variable is included in the estimates of the fixed portion of the model ( β .)

How to cluster datasets with discrete and continuous variables?

The idea is creating a synthetic dataset by shuffling values in the original dataset and training a classifier for separating both. During classification you will get an inter-sample distance matrix, on which you could test your favorite clustering algorithm.

Which is the best clustering algorithm for continuous data?

They are ‘hard clustering’ algorithms – every data point is exclusively assigned to one cluster. The number of clusters must be predefined by the analyst. Because means/medians are used for clustering, these algorithms are only appropriate for continuous data. Therefore, they are unsuitable for categorical data.

How to cluster mixed categorical and continuous data?