Advances in Data Mining and Modeling: Hong Kong 27 - 28 June - download pdf or read online

By Wai-Ki Ching, Michael Kwok-Po Ng

ISBN-10: 9812383549

ISBN-13: 9789812383549

ISBN-10: 9812704957

ISBN-13: 9789812704955

Facts mining and knowledge modelling are lower than quickly improvement. due to their large purposes and learn contents, many practitioners and teachers are drawn to paintings in those components. with a purpose to selling verbal exchange and collaboration one of the practitioners and researchers in Hong Kong, a workshop on info mining and modelling was once held in June 2002. Prof Ngaiming Mok, Director of the Institute of Mathematical learn, The collage 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 awarded on the workshop. The papers fall into major different types: facts mining and information modelling. facts mining papers care for trend discovery, clustering algorithms, category and sensible purposes within the inventory marketplace. facts modelling papers deal with neural community versions, time sequence types, statistical types and sensible purposes.

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Extra resources for Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002

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Therefore, the rank ( T I , . . ,T ~ should ) be a good approximation to the true ordering in the UDS solution. We illustrate this method by two examples taken from Robinson 7: the 8 x 8 dissimilarity matrix for the Mani collection of archaeological deposits, and a 17 x 17 matrix for the Kabah collection. These examples + 43 were also used as test cases by Hubert and Arabie Leung and Tse 5 . 4, Pliner and Lau, Table 1. Dissimilarity matrix from Mani collection. 58 Table 1 gives the 8 x 8 dissimilarity matrix and their row total Si.

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The fuzzy versions of k-means type algorithms The k-means, k-modes and k-prototypes algorithms are called hard clustering algorithms because they assign each data object into only one cluster. However, objects in different clusters often overlap at boundaries. Instead of assigning each boundary object into only one cluster, the boundary objects can be assigned to more than one cluster with different confidence levels. The fuzzy versions of these algorithms are designed for this purpose. Let X be a set of n objects described by m attributes.

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Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002 by Wai-Ki Ching, Michael Kwok-Po Ng


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