  With the dawn of the Big Data era, data sets are growing rapidly. Data is
streaming from everywhere - from cameras, mobile phones, cars, and other
electronic devices. Clustering streaming data is a very challenging problem.
Unlike the traditional clustering algorithms where the dataset can be stored
and scanned multiple times, clustering streaming data has to satisfy
constraints such as limit memory size, real-time response, unknown data
statistics and an unknown number of clusters. In this paper, we present a novel
online clustering algorithm which can be used to cluster streaming data without
knowing the number of clusters a priori. Results on both synthetic and real
datasets show that the proposed algorithm produces partitions which are close
to what you could get if you clustered the whole data at one time.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning