Performance Evaluation of Data Stream Clustering Algorithm on Parameter Specification

Parameter specification remains a difficult task in data stream clustering as density-based algorithms hyperparameters tuning to their optimal values are often difficult to determine. This paper investigates the sensitivity of parameter tuning on DenStream, a data stream clustering algorithm. The effects on different noise levels are evaluated for the DenStream and two chosen benchmark algorithms, CluStream and ClusTree algorithms, using both synthetic and real-world datasets, and several performance metrics. It was found that DenStream outperforms CluStream and ClusTree on some of those metrics.