FITTING STRAIGHT-LINES TO POINT PATTERNS
FITTING STRAIGHT-LINES TO POINT PATTERNS
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DOI：
10.1016/0031-3203(84)90045-1
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发表时间：
1984-01-01
影响因子：
8
通讯作者：
RAFTERY, AE
中科院分区：
文献类型：
--
作者：
MURTAGH, F;RAFTERY, AE
在许多类型的点模式中，线性特征是最令人感兴趣的。本文提出了一种非常通用的算法，用于确定具有大线性的点的非重叠簇。给定一组点，该算法依次合并对簇或对点，合并准则中包含相邻性和线性性。该算法是对广泛使用的Ward最小方差分层聚类方法的推广。通过文献中的实例说明了该算法在生物特征识别和字符识别中的应用。
In many types of point patterns, linear features are of greatest interest. A very general algorithm is presented here which determines non-overlapping clusters of points which have large linearity. Given a set of points, the algorithm successively merges pairs of clusters or of points, encompassing in the merging criterion both contiguity and linearity. The algorithm is a generalization of the widely-used Ward's minimum variance hierarchical clustering method. The application of this algorithm is illustrated using examples from the literature in biometrics and in character recognition.