问题描述
我是ELKI的初学者，我一直在使用它来对.csv文件中的10K纬度点进行聚类.正确设置后，我想放大到1MM点.
I'm an ELKI beginner, and I've been using it to cluster around 10K lat-lon points from a .csv file. Once I get my settings correct, I'd like to scale up to 1MM points.
我正在使用带有LngLatDistanceFunction的OPTICSXi算法
I'm using the OPTICSXi algorithm with LngLatDistanceFunction
我一直在阅读有关通过STR批量加载启用R *-树索引"的内容，以查看性能方面的巨大改进.这些教程对我没有多大帮助.
I keep reading about "enabling R*-tree index with STR bulk loading" in order to see vast improvements in performance. The tutorials haven't helped me much.
有关如何实现此功能的任何提示?
Any tips on how I can implement this feature?
推荐答案
在二维数据上使用空间R *索引的建议参数为:
The suggested parameters for using a spatial R* index on 2 dimensional data are:
-db.index tree.spatial.rstarvariants.rstar.RStarTreeFactory
-pagefile.pagesize 512
-spatial.bulkstrategy SortTileRecursiveBulkSplit
对于高维数据，需要更大的页面尺寸. 512-1024字节的页面大小似乎是二维数据的最佳选择，但它的确也取决于您的数据.
For higher dimensional data, larger page sizes are necessary. A page size of 512-1024 bytes seems to be the sweet spot for 2 dimensional data, but it does depend on your data, too.
要离散化群集，可以使用Xi提取:
To discretize clusters, you can use the Xi extraction:
-algorithm clustering.optics.OPTICSXi -opticsxi.xi 0.005
要从OPTICS的索引加速中受益，请为您的应用选择尽可能小的epsilon.该参数以米表示，其中所有地球模型都位于ELKI中.
To benefit from index acceleration with OPTICS, choose epsilon as small as possible for your application. The parameter is in meters with all the earth models in ELKI.
-opticsxi.algorithm OPTICSHeap
-algorithm.distancefunction geo.LatLngDistanceFunction
-optics.epsilon 2000.0 -optics.minpts 10
最多使用2公里的距离.
uses 2 km distances maximum.
确保区分latitude,longitude和longitude,latitude.这两个命令都被使用，并且您需要使用正确的距离函数:
Make sure to distinguish latitude,longitude and longitude,latitude. Both orders are used, and you need to use the right distance function:
geo.LatLngDistanceFunction
geo.LngLatDistanceFunction
这篇关于如何使用ELKI进行索引-OPTICS群集的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！