14. A computer implemented method for machine learning by a first entity supporting a rule based system, wherein each rule is associated with a centre of a cluster of data points in a combined rule input and rule output data space, the method comprising: receiving a time series data item as input; determining whether a data point for the time series input data item increases or decreases the spatial density for each previously existing rule; if the data point does increase or decrease the spatial density for every previously existing cluster, then creating a new cluster and associated rule, otherwise, if the data point does not increase the spatial density for every previously existing cluster, or does not decrease the spatial density for every previously existing cluster, then not creating a new cluster; and repeating the method for a next time series data item received.
16. The method of claim 14 , and further comprising recursively recalculating a measure of the spread of each cluster in the data space.