By Wai-Ki Ching, Michael Kwok-Po Ng

Facts mining and knowledge modelling are below speedy improvement. due to their broad functions and examine contents, many practitioners and teachers are drawn to paintings in those parts. in order to selling communique 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 collage 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 comprises chosen papers provided on the workshop. The papers fall into major different types: information mining and knowledge modelling. facts mining papers take care of trend discovery, clustering algorithms, class and functional purposes within the inventory industry. information modelling papers deal with neural community types, time sequence versions, statistical types and sensible purposes.

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We leave them for future research. References 1. J. Chen and A. K. Gupta, Parametric Statistical Change Point Analysis, Birkhauser (2000). 2. J. de Leeuw and W. J. C. Lingoes, Ann Arbor, MI: Mathesis, 735-752. 3. L. Guttman, A General Nonmetric Technique for Finging the Smallest Coordinate Space for a Configuration of Points, Psychometrika, 33, 469-506 (1968). 4. J. Heiser and P. , J. de Leeuw, W. Heiser, J. Meulman, and F. Critchley, Leiden, The Netherlands: DSWO Press, 181-196 (1986). 5. K. Lau, P.

Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. Database Table 2. DatabaseTable 2. Database Table 2. Database Table 2. Database References 1. 2. 3. 4. 5. 6. 7. 8. 9. , Gehrke, J, Gunopulos, D. , Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of SIGMOD Conference, (1998). , Cluster Analysis for Applications.

Because all distances between objects are known, it is straightforward to calculate the coordinates of all objects on the frrst axis. To calculate the coordinates of objects on the second axis, we first need to calculate the distances between objects in the reduced (n - 1) dimensional space. The distances can be calculated from the distances in the original n dimensional space and the coordinates on the first axis, as follows: 33 (dl:,j)2 = ( di , j )2 -(xi - x j y . ,N Z,J whered,’,jis the distance between obejcts Oi and Oj in the reduced (n - 1) dimensional space, di,jis the distance between obejcts Oi and Oj in the original n dimensional space, 4 ,xj are the coordinates of Oi and Oj on the first axis.

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