By Wai-Ki Ching, Michael Kwok-Po Ng
Info mining and information modelling are lower than quick improvement. due to their huge purposes and study contents, many practitioners and teachers are drawn to paintings in those components. to be able to selling conversation and collaboration one of the practitioners and researchers in Hong Kong, a workshop on facts mining and modelling was once held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical examine, The college of Hong Kong, and Prof Tze Leung Lai (Stanford University), C.V. Starr Professor of the college of Hong Kong, initiated the workshop. This paintings includes chosen papers provided on the workshop. The papers fall into major different types: facts mining and knowledge modelling. info mining papers take care of trend discovery, clustering algorithms, category and functional purposes within the inventory marketplace. info modelling papers deal with neural community types, time sequence types, statistical types and functional functions.
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Extra info for Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002
Based on large jumps or gaps in the coordinates, we can identify possible boundaries of clusters. In this paper, only the basic idea of UDS clustering is presented. There are several problems and extensions worth further investigation: (1) In the iris data or the simulated ring example, we used the Euclidean metric to represent the interpoint distances. This is suitable only for continuous variables. When the variables are binary, categorical or of mixed type, other suitable metric should be used.
Our proposed method actually improves the final UDS solution obtained. Once the coordinates of these n objects are obtained, we can test whether there are large jumps or gaps in the coordinates. In Section 3, we introduce the Schwarz Information Criterion (SIC) given in Chen and Gupta for detecting jumps or gaps in the coordinates. We illustrate our proposed method by the Fisher's famous Iris data as well as by a simulated data. We find that our method outperform the K-means clustering method. Conclusive remarks and further extensions are discussed in Section 4.
3. Apply 0 to partition S , into k clusters. 4. ) 5. If the partition is accepted, go to step 6, otherwise, select a new k and go to step 3. 6 . Attach the clusters as the children of the partitioned node. Select one as the current node S,. 7. Validate S , to determine whether it is a terminal node or not. 8. If it is not a terminal node, go to step 2. If it is a terminal node, but not the last one, select another node as the current node S,, which has not been validated, and go to step 7. If it is the last terminal node in the tree, stop.