  The scale of systems employed in industrial environments demands a large
number of sensors to facilitate meticulous monitoring and functioning. These
requirements could potentially lead to inefficient system designs. The data
coming from various sensors are often correlated due to the underlying
relations in the system parameters that the sensors monitor. In theory, it
should be possible to emulate the output of certain sensors based on other
sensors. Tapping into such possibilities holds tremendous advantages in terms
of reducing system design complexity. In order to identify the subset of
sensors whose readings can be emulated, the sensors must be grouped into
clusters. Complex systems generally have a large quantity of sensors that
collect and store data over prolonged periods of time. This leads to the
accumulation of massive amounts of data. In this paper we propose an end-to-end
algorithmic solution, to realise virtual sensors in such systems. This
algorithm splits the dataset into blocks and clusters each of them
individually. It then fuses these clustering solutions to obtain a global
solution using an Ant Colony inspired technique, FAC2T. Having grouped the
sensors into clusters, we select representative sensors from each cluster.
These sensors are retained in the system while the other sensors readings are
emulated by applying supervised learning algorithms.
