By Wai-Ki Ching, Michael Kwok-Po Ng
Info mining and knowledge modelling are lower than quickly improvement. due to their extensive functions and examine contents, many practitioners and lecturers are drawn to paintings in those components. so as to selling verbal exchange and collaboration one of the practitioners and researchers in Hong Kong, a workshop on information mining and modelling was once held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical study, The college of Hong Kong, and Prof Tze Leung Lai (Stanford University), C.V. Starr Professor of the collage of Hong Kong, initiated the workshop. This paintings comprises chosen papers provided on the workshop. The papers fall into major different types: information mining and knowledge modelling. information mining papers take care of development discovery, clustering algorithms, type and useful purposes within the inventory industry. info modelling papers deal with neural community types, time sequence types, statistical versions and functional purposes.
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Extra info for Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002
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However, our final DDC model is not necessarily to be a partition. In the final DDC model, we often drop certain clusters from a clustering. For example, some leaves in Figure 2 do not have class symbols. These clusters contain few objects in several classes. These are the objects, which are located in the boundaries of other clusters. From our experiment, we found that dropping these clusters from the model can increase the classification accuracy. Table 1 shows four public data sets taken froin the UCI machine learning data repositoryc, which were used to test the DCC models against some other classifiers.