Multi-level K-Means Density-Based Flow Clustering Algorithm for Data Stream Clustering

Abstract

Data stream clustering is an active area of research that has recently emerged with the goal of discovering new knowledge from a large amount and variability of constantly generated data. In this context, many researchers have proposed different algorithm for unsupervised learning that clusters multiple data streams. There is a need for a more efficient and efficient data analysis method. This paper introduces a multi-level K-Means density-based flow clustering algorithm (MKDCSTREAM) for clustering problems. This approach proposes to view the problem of clustering as an optimization process hierarchy that follows different levels, from unrefined to subtle. In the clustering problem, for the solution, divide the problem into parts by following different levels to make the first clustering a coarser problem than calculated. Coarse problem clustering is mapped level by level and improves the clustering of the original problem by improving intermediate clustering using the general K-means algorithm. Compare the performance of the hierarchical approach with its single-tier approach using tests with a set of data-sets collected from different areas.