Clustering data streams is an emerging challenge with a wide range of applications in areas
including Wireless Sensor Networks, the Internet of Things, finance and social media. In an
evolving data stream, a clustering algorithm is desired to both (a) assign observations to
clusters and (b) identify anomalies in real-time. Current state-of-the-art algorithms in the
literature do not address feature (b) as they only consider the spatial proximity of data, which
results in (1) poor clustering and (2) poor demonstration of the temporal evolution of data in …