I have a location collection tool which send me location data of our survey. Surveyors send multiple data of a single item. Such as they send numarous data for a school like the following image. From the image we can clearly identify few locations those are actually not collected properly or inaccurate location came due to GPS accuracy. Here I have marked red circle which location need to be excluded and the green circle indicates this is the probable location. IF we create a centroid based on all points then it will not indicates the probable correct location due to few highly inaccurate data. I wonder, if any method or tool can exclude the wrong location and create a cluster based on densed location points to create a centroid then the centroid can be established as probable right location of the surveyed item. I have millions of item those have hundreds of location each. I came to know that k-means do something like that but it does not do on a single item. I have four column in table.
Is there any method to create item wise clustering where every cluster have a value which indicates its containing points to get the probable right location?