Multi-objective Semi-supervised clustering for finding predictive clusters

This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable. The main goal is partitioning data points so that observations in each cluster are similar and the outcome variable can be predicted using these clusters simultaneously. We model this semi-supervised clustering problem as a multi-objective optimization problem with considering deviation of data points in clusters and prediction error of the outcome variable as two objective functions to be minimized. For finding optimal clustering solutions, we employ a non-dominated sorting genetic algorithm II approach and local regression is applied as the prediction method for the output variable. For comparing the performance of the proposed model, we compute seven models using five real-world data sets. Furthermore, we investigate the impact of using local regression for predicting the outcome variable in all models and examine the performance of the multi-objective models compared to single-objective models.

Automated summary

This study focuses on clustering problems and aims to predict outcome variables by partitioning data points into similar clusters using a multi-objective optimization approach. Local regression is used to predict the outcome variable, and the performance of the multi-objective models is compared to single-objective models.