The Recommendation Architecture (RA) model is a new connectionist approach simulating some aspects of the human brain. Application of the RA to a real world problem is a novel research problem and has not been previously addressed in literature. Research conducted with simulated data has shown much promise for the Recommendation Architecture model’s ability in pattern discovery and pattern recognition. This thesis investigates the application of the RA model for text mining where pattern discovery and recognition play an important role. The clustering system of the RA model is examined in detail and a formal notation for representing the fundamental components and algorithms is proposed for clarity of understanding. A software simulation of the clustering system of the RA model is built for empirical studies. In the argument that the RA model is applicable for text mining the following aspects of the model are examined. With its pattern recognition ability the clustering system of the RA is adapted for text classification and text organization. As the core of the RA model is concerned with pattern discovery or identification of associative similarities in input, it is also used to discover unsuspected relationships within the content of documents. How the RA model can be applied to the problems of pattern discovery in text and classification of text is addressed demonstrating results from a series of experiments. The difficulties in applying the RA model to real life data are described and several extensions to the RA model for optimal performance are proposed from the insights obtained from experiments. Furthermore, the RA model can be extended to provide user-friendly interpretation of results. This research shows that with the proposed extensions the RA model can be successfully applied to the problem of text mining to a large extent. Some limitations exist when the RA model is applied to very noisy data, which are also demonstrated here.

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