On Density-Based Clustering Algorithms over Evolving Data Streams: A Summarization Paradigm
Amineh Amini, Teh Ying Wah · Applied Mechanics and Materials · 2012
Clustering is one of the prominent classes in the mining data streams. Among various clustering algorithms that have been developed, density-based method has the ability to discover arbitrary shape clusters, and to detect the outliers. Recently, various algorithms adopted density-based methods for clustering data streams. In this paper, we look into three remarkable algorithms in two groups of micro-clustering and grid-based including DenStream, D-Stream, and MR-Stream. We compare the algorithms based on evaluating algorithm performance and clustering quality metrics.