Enhancing K-means Clustering Results with Gradient Boosting: A Post-Processing Approach

Abstract

As the volume and complexity of data continue to grow exponentially, finding efficient and accurate clustering algorithms has become crucial for many applications. K-means clustering is a widely used unsupervised machine learning technique for data analysis and pattern recognition. Despite its popularity, k-means suffers from certain limitations, such as sensitivity to initial conditions, difficulty in determining the optimal number of clusters, and the potential for misclassification. This research paper proposes an enhanced approach for improving the accuracy and performance of the k-means clustering algorithm by incorporating post-processing techniques using a gradient boosting algorithm. The proposed method comprises training the gradient boosting model on the labeled training set, i.e., the samples with correct cluster assignments obtained from the k-means algorithm, to predict the correct cluster assignments for the misclassified samples in the testing set. This results in refined cluster assignments for the testing set. The k-means algorithm is only used initially to cluster the data and obtain initial cluster assignments. The effectiveness of the proposed approach is validated through experiments on several benchmark datasets, and the results show a significant improvement in clustering accuracy and robustness compared to the standard k-means algorithm. The proposed approach has the potential to enhance the performance of k-means in various real-world applications and domains.