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.

I. INTRODUCTION

The knowledge discovery based on numerical and other data obtained from the real environment is a fast growing research area [1]–[8]. The general objective here is to aggregate, granulate or generalize the data in a specific way so that a significant part of the information from the past data to be available and usable during the further time periods for approximation, generalization, prediction and another knowledge discovery. The usual problem here is that data from the environment come continuously as large streams of information [1], [3], [4], or as big chunks of data [2], [7] which makes it impossible to keep all time such huge information. Here the concept of the Evolving Knowledge Based Systems is the most appropriate way for knowledge extraction and knowledge management with time, since such systems (by definition) are able to properly grow, update, prune and forget the information in the Knowledge Base over time.