By Wai-Ki Ching, Michael Kwok-Po Ng
Facts mining and knowledge modelling are less than quick improvement. as a result of their large functions and study contents, many practitioners and lecturers are drawn to paintings in those parts. with the intention 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 examine, 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 awarded on the workshop. The papers fall into major different types: info mining and knowledge modelling. info mining papers care for trend discovery, clustering algorithms, type and sensible purposes within the inventory industry. info modelling papers deal with neural community versions, time sequence versions, statistical types and useful purposes.
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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. Academic Press, (1973). C, Pattern Recognition with Fuzzy Objective Function. Plenum Press (198 1). Bobrowski, L. , c-Means Clustering with the 11 and 1. Norms', IEEE Transactions on Systems, Man and Cybernetics 21, 545-554 (1991). Chen, Ning, Chen, An and Zhou, Long-xiang, Fuzzy k-prototypes algorithm for clustering mixed numeric and categorical valued data.
In real applications of clustering, we are required to perform three tasks, (1) partitioning data sets into clusters, (2) validating the clustering results and (3) interpreting the clusters. Various clustering algorithms have been developed for 25 26 the first task. The standard hierarchical clustering methods  and the kmeans algorithm  are well known clustering techniques not only in data mining but also in other disciplines. To deal with large and complex data often encountered in data mining applications, a number of new clustering algorithms have been developed in recent years, for example, CLIQUE [l], CLARANS , BIRCH , DBSCAN  and the k-means extension algorithms [lo].
N is first constructed. 3). This solution is used as the initial configuration for the second stage with E = € 2 . The solution in the second stage is used as the initial configuration for the third stage and so on. This process continue up to the N t h stage. Pliner’s smoothing algorithm provides a better solution than Guttman’s updating algorithm but it also requires longer computational time as well. Therefore, a good starting configuration is very important to these algorithms. In practice, these algorithms are repeated many times with randomly chosen starting configuration and pick the best solution among them.
Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002 by Wai-Ki Ching, Michael Kwok-Po Ng