A locally sensitive method for cluster analysis

In this paper a new method of mode separation is proposed. The method is based on mapping of data points from the N-dimensional space onto a sequence so that the majority of points from each mode become successive elements of the sequence. The intervals of points in the sequence belonging to the respective modes of the p.d.f. are then determined from a function generated on this sequence. The nuclei of the modes formed by the elements of these intervals are then used to obtain separating surfaces between the modes and so to partition the data set with multimodal probability density function into unimodal subsets.