Temporal and spatial Evolving Knowledge Base system with sequential clustering

This paper proposes a computational scheme of a novel Evolving Knowledge Base system that is able to gradually grow and update spatially and temporally. The main assumption is that the input information comes from the real environment in the form of chunks of data (not single data points). Therefore the whole system works in a quasi-real time. Each chunk of data is used for extraction of the so called knowledge items, which is done by a specially introduced sequential clustering algorithm. It is able to discover the separate knowledge items sequentially, in decreasing order of their size. Another important block of the proposed evolving knowledge base system is the updating algorithm, It is in charge of managing the Knowledge Base over time and performs (when necessary) one of the three types recursive computations, namely: learning, relearning and forgetting. The flexibility and the degree of generality of the proposed evolving system is illustrated on a specially constructed example that resembles a real case of data flow coming as a sequence of 20 chunks of data. These data exhibit evolving behavior during the sampling periods and the knowledge Base system is able to catch such behavior by properly updating its parameters. These results show the way of different possible practical applications.