Can you use K means clustering on 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.

Which algorithm is best for categorical data?

Logistic Regression is a classification algorithm so it is best applied to categorical data.

What is not cluster analysis?

The main idea… Non-hierarchical cluster analysis aims to find a grouping of objects which maximises or minimises some evaluating criterion. Many of these algorithms will iteratively assign objects to different groups while searching for some optimal value of the criterion.

Why is it difficult to handle categorical data for clustering?

The focus of research in clustering data has moved from numeric data to categorical data because almost all real data is categorical. Clustering categorical data is a bit difficult than clustering numeric data because of the absence of any natural order, high dimensionality and existence of subspace clustering.

How do you cluster a categorical variable in Python?

2 Answers

1. Use OneHotEncoder. You will transform categorical feature to four new columns, where will be just one 1 and other 0.
2. Use OrdinalEncoder. You transform categorical feature to just one column.
3. Use transformation that I call two_hot_encoder. It is similar to OneHotEncoder, there are just two 1 in the row.

How do you do cluster analysis with categorical variables?

Unlike Hierarchical clustering methods, we need to upfront specify the K.

1. Pick K observations at random and use them as leaders/clusters.
2. Calculate the dissimilarities and assign each observation to its closest cluster.
3. Define new modes for the clusters.
4. Repeat 2–3 steps until there are is no re-assignment required.

Can neural network handle categorical data?

The Challenge With Categorical Data Machine learning algorithms and deep learning neural networks require that input and output variables are numbers. This means that categorical data must be encoded to numbers before we can use it to fit and evaluate a model.