Hybrid Approaches in AI: Combining K-Modes Clustering with Machine Learning for Improved Model Performance

Leveraging hybrid approaches has proved to be a strong strategy in Artificial Intelligence, especially when dealing with complex sets of data, in order to improve model performances. This paper attempts to discuss the integration of K-Modes clustering with machine learning models further, highlighting unique advantages that are brought by the integration of the best algorithm—K-Modes on categorical data—with machine learning techniques for improved predictive accuracy and efficiency. While preprocessing with K-Modes groups data into distinct clusters, machine learning algorithms can then be configured to capture unique patterns in each cluster, thus providing more refined predictions and robust model performance. It thus represents a hybrid approach that is being tested on various datasets for their potential to yield improved results in customer segmentation, recommendation systems, and other categorical data analyses in healthcare. The results highlight that this K-Modes clustering step is an important preprocessing step on the path to both interpretable and performing AI models on categorical data.