Combinatorial Methods for Cluster Analysis
نویسنده
چکیده
Let FX(x) be a cumulative distribution function on Ek; k-dimensional Euclidean space, k 1: We assume that FX(x) is absolutely continuous with respect to k-dimensional Lebesgue measure and denote the corresponding probability density function by fX(x): Assume that a random sample of size n has been obtained from FX(x) and denote the realizations by x1; x2; : : : ; xn: In cluster analysis, similar objects are to be placed in the same cluster. We will interpret similarity as being close with respect to some distance on Ek. The relationship between graph theory and cluster analysis has been described in the books by Bock (1974) and Godehardt (1990). Mathematical results related to those used here are given in Eberl and Hafner (1971), Hafner (1972), Godehardt and Harris (1995), Godehardt and Harris (1998) and Maehara (1990).
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تاریخ انتشار 1999