Comparative Analysis of Optimization Strategies for K-means Clustering in Big Data Contexts: A Review
It provides a comparative guide for practitioners and researchers on optimizing K-means for big data, but it is incremental as it reviews existing methods.
This paper tackled the scalability issues of K-means clustering in big data by comparing optimization techniques like parallelization and sampling, finding that different methods suit different datasets with trade-offs in speed and accuracy.
This paper presents a comparative analysis of different optimization techniques for the K-means algorithm in the context of big data. K-means is a widely used clustering algorithm, but it can suffer from scalability issues when dealing with large datasets. The paper explores different approaches to overcome these issues, including parallelization, approximation, and sampling methods. The authors evaluate the performance of various clustering techniques on a large number of benchmark datasets, comparing them according to the dominance criterion provided by the "less is more" approach (LIMA), i.e., simultaneously along the dimensions of speed, clustering quality, and simplicity. The results show that different techniques are more suitable for different types of datasets and provide insights into the trade-offs between speed and accuracy in K-means clustering for big data. Overall, the paper offers a comprehensive guide for practitioners and researchers on how to optimize K-means for big data applications.