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The k-modes as Clustering Algorithm for Categorical Data Type
The explanation of the theory and its application in real problems
The basic theory of k-Modes
In the real world, the data might be having different data types, such as numerical and categorical data. To perform a certain analysis, for instance, clustering analysis, we should consider the data type in the data we have. The clustering algorithm commonly used in clustering techniques and efficiently used for large data is k-Means. But, it only works for the numerical data. It’s actually not suitable for the data that contains the categorical data type. So, Huang proposed an algorithm called k-Modes which is created in order to handle clustering algorithms with the categorical data type.
The modification of k-Modes as the improvement of k-Means for categorical variables can be found here.
The application of k-Modes
There are a few modules used for performing data preprocessing, data exploration with explanatory data analysis, and the k-Modes…
