W. Kritboonyalai and V. Avatchanakorn (Thailand)
Data mining, Fuzzy C-mean algorithms, Clustering, Knowledge Discovery
With the rapid growth of databases, the automated knowledge discovery in database (KDD) is becoming more and more important for the purpose of discovering the interesting information in databases. Data mining is the most crucial discovery stage of the KDD process. This paper proposes the data mining technique using fuzzy c-mean (FCM) as a clustering engine for extracting the patterns of interest in database systems. The technique can be adjusted to individual requirements of specific applications by choosing appropriate parameters and databases which provide the information of interest. The effectiveness of the proposed technique is tested by applying it to the strategic planning for public hospital in Thailand. The results obtained from the computer simulation prove that the FCM based data mining technique provides and effective cluster extraction from the database.
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