A New Incremental Cluster Validity Index for Streaming Clustering Analysis
Omar A. Ibrahim, James M. Keller, Mihail Popescu · 2019
In this paper, we present an incremental version of the Partition Coefficient and Exponential Separation (PCAES) cluster validity index in the context of streaming data analysis. Incremental PCAES (iPCAES) can be used to monitor evolving structures in streaming data. We investigate the use of the proposed index to understand and analyze the performance of the MU Streaming Clustering (MUSC) algorithm. Synthetic and real-life streaming datasets are used to demonstrate the benefits that can be drawn from such indices such as the appearance of a new structure in the data stream, handling of outlier data samples, and the effect of the streaming sample order on the resultant cluster history. We compare the performance of iPCAES index with the incremental Davies-Boudin index (iDB) because iDB was found to be the most stable among other incremental indices that offer comparable approaches.