By Wai-Ki Ching, Michael Kwok-Po Ng
Info mining and knowledge modelling are less than speedy improvement. due to their broad purposes and examine contents, many practitioners and lecturers are interested in paintings in those components. on the way to selling conversation and collaboration one of the practitioners and researchers in Hong Kong, a workshop on info mining and modelling used to be 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 includes chosen papers awarded on the workshop. The papers fall into major different types: info mining and information modelling. information mining papers care for development discovery, clustering algorithms, class and functional purposes within the inventory marketplace. facts modelling papers deal with neural community types, time sequence types, statistical types and useful functions.
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Extra info for Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002
Therefore, the DCC model can give an accurate classification result. 5. Conclusions In this paper, we have reviewed the family of the k-means clustering algorithms, which are mostly used in data mining. Efficiency, easy to implement and easy to use are the most attractive features of the k-means algorithm in real data mining applications. Extensions to the original k-means algorithm enable the k-means paradigm to be used in clustering data in more complex data domains. We have presented a visual cluster validation method for data mining application.
J be the number of objects having the kth category cu in attribute A, and f,(Aj = ck,j Theorem rick, j I x)= the relative frequency of category cu in X. n 1. m. I iff The proof is given in [ 101. If we use Eq. (4) to solve P 1 and Theorem 1 to solve P2, we can still use the basic algorithm in the above section to minimize Eq. (3). Now, we can integrate the k-means and k-modes algorithms into the kprototypes algorithm to cluster the mixed-type objects. The cost function of the k-prototypes algorithm is defined as follows: 30 The first term follows the k-means algorithm for numeric attributes and the second term the k-modes algorithm for categorical attributes.
3). Now, we can integrate the k-means and k-modes algorithms into the kprototypes algorithm to cluster the mixed-type objects. The cost function of the k-prototypes algorithm is defined as follows: 30 The first term follows the k-means algorithm for numeric attributes and the second term the k-modes algorithm for categorical attributes. The weight y is used to avoid favoring either type of attribute. The centers of clusters contain both numeric and categorical values so the k vectors of cluster centers are named k prototypes.
Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002 by Wai-Ki Ching, Michael Kwok-Po Ng