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The aim of the study is to develop a framework that integrates knowledge management (KM) and decision support systems (DSS) by using knowledge discovery techniques (KDT). KDT are applied for achieving conversions among different types of knowledge and also creating new models from previously defined ones. Extracted neural network rules are stored into a model base for achieving knowledge externalization. CLIQUE algorithm, suitable for clustering high dimensional data, is used for generating explicit knowledge by combining decision rules in the model base. The case base reasoning (CBR) paradigm is utilized for the other types of knowledge conversions, internalization and socialization. CBR enables to solve newly defined problem with the help of previous rules. The applicability of the proposed framework is demonstrated by an experimental study in which forecasting the change in US Dollar/Turkish Lira exchange rate is illustrated.

Developing a Framework for Integrating Knowledge Management and Decision Support Systems: Application to Time Series Forecasting