Multilevel K-Means Density Based Flow Clustering Algorithm for Data Streams

Abstract

The 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, different-different algorithm for unsupervised learning that clusters multiple data streams has been proposed by many researchers. 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 is a optimization process hierarchy that follow different levels, from unrefined to subtle. In the clustering problem, for the solution first divide the problem in 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 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 datasets collected from different areas.