Multi-objective Semi-supervised Clustering for Finding Predictive Clusters

TL;DR

This paper introduces a multi-objective semi-supervised clustering method that finds compact, outcome-predictive clusters by optimizing data similarity and prediction error simultaneously, using a genetic algorithm and local regression.

Contribution

It proposes a novel multi-objective optimization framework for semi-supervised clustering that integrates outcome prediction, employing genetic algorithms and local regression for improved predictive clusters.

Findings

The proposed model outperforms single-objective models on five real-world datasets.

Using local regression enhances outcome prediction accuracy.

Multi-objective approach yields more informative clusters for prediction.

Abstract

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.