ABSTRACT
This chapter defines the streaming process, first, in terms of the real world and then in terms of the main computational contribution to this discipline for streaming data-viz., cluster footprints. It begins with an overview of some notable work in streaming clustering. It contains examples of five stream clustering algorithms that should suffice to establish why we need some changes in our thinking about this topic. The chapter discusses some recent research about data visualization and stream monitoring functions in stream processing that forms a sort of bridge between the semantics of classical clustering and the language that needs to make its way into this new field. Visualization of the streaming data with inc-siVAT provides some insight into structural tendencies in the streaming data and is independent of the dimensionality of the streaming data.
