Multi-objective Semi-supervised Clustering for Finding Predictive Clusters
This work addresses the challenge of integrating clustering and prediction for domain-specific applications, but it appears incremental as it combines existing methods like genetic algorithms and local regression.
The authors tackled the problem of finding compact clusters that are also predictive of an outcome variable by modeling it as a multi-objective optimization problem, using a genetic algorithm and local regression, and tested it on five real-world datasets with comparisons to seven other models.
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 predicated 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 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.