通过不使用原始研究论文中已经建议的算法进行DBSCAN的eps估计

我必须使用python实现DBSCAN，并且epsilon估计一直存在问题，因为原始研究论文中已建议的方法假定数据集的分布像斑点一样，而在我的情况下，它更像是一种可固化的可治愈数据有一定间隔的跳跃。跳跃导致DBSCAN在跳跃之间的间隔中形成各种数据集的不同聚类（这对我来说足够好），但是针对不同数据集的动态epsilon计算不会产生期望的结果，因为这些点倾向于位于一条直线上。许多时间间隔，并且更改 k值会导致eps值发生较大变化。

I have to implement DBSCAN using python, and the epsilon estimation has been posing problems as the already suggested method in the original research paper assumes blob like distribution of the dataset, where as in my case it is more of a cure fittable data with jumps at some intervals. The jumps cause the DBSCAN to form different clusters of various datasets in the intervals between jumps(which is good enough for me), but the epsilon calculation dynamically for different datasets does not produce desired results as the points tend to lie on a straight line for many intervals, and changing 'k' value cause a considerable change in the eps value.