Knowledge Discovery in Databases
Knowledge Discovery in Databases
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Data mining or knowledge discovery in database (KDD) is motivated by large amounts of computerized data and has been attracted a lot of interest in various areas. One area is to extract useful and predictive information from a huge financial data database so that investors can be more informed and makes more profitable investments. The efficiency of the information extraction has become the most concern problem when performing the extraction process. In this paper, we demonstrate how to apply conceptual clustering (hierarchical clustering algorithm), a data mining technique, on the Chinese and Hong Kong stock market's data. Conceptual hierarchical tree and cluster information table will be generated to give the concept to the clusters for further analysis in the subsequent mining process.