In this thesis we present two new methods for discovering knowledge from ordered datasets using Baldwin's mass assignment and the Fril programming language. We derive our methods from human observation and knowledge handling computing techniques. We define this combination of observed human behaviour and conventional computing as humanist computing. The first method represents trends in ordered databases using a new perception based fuzzy feature. This feature is implemented as a fuzzy set which we call the trend fuzzy set. The trend fuzzy set approach is shown in application to simple regular time series and the real world problem of sunspot series modelling. Results show improvement in dataset modelling using this approach. The second method defines a new fuzzy rule structure called the recurrent interacting belief updating fuzzy rule. This rule structure is developed by combining simple belief updating with a tailored fuzzy rule. Results in application to particle classification and facial feature discovery show significant improvement. Both of these methods are based on the humanist computing approach to knowledge discovery. They both generate summaries of data in clear concise linguistic forms. These summaries are glass box models of the data.
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