A novel self-directed learning framework for cluster ensemble

Abstract

This study looked into the problems that occur when a clustering or cluster ensemble model with unsupervised or semi-supervised learning is used in a real-world setting. However, validating the obtained results is a challenging task. Therefore, we propose a self-directed learning (SDL) framework for cluster ensemble, which improves the traditional ensemble framework by assisting the consensus function in achieving the highest assessment of clustering performance. The SDL is built on two models: predicting test set labels (PTL) and detecting best results (DBR). The PTL model assists in predicting the test set labels; the PTL is based on consistently ensembling the outcomes until it obtains satisfactory results. The DBR identifies the correct answers for each data object when a single model provides several outcomes for a single dataset. To highlight the power of our proposed framework, we consider multiple performance measurements; one of them is termed as correction ratio (CR). We compare SDL with several cluster ensemble models. The results show that the proposed framework outperforms other models with 18% accuracy on average and achieves similar results for other performance indicators.