Clustering Crime Data which has {latitute, longitude, crime-type} tuples I have a data set which has thousands of rows of {latitute, longitude, crime-type} tuples. 

Sample Data:

41.757366519   -87.642992854   THEFT
41.910469677   -87.585822373   ROBBERY
41.751270452   -87.690708662   BURGLARY
41.757366519   -87.642992854   THEFT
41.757366519   -87.642992854   THEFT
..             ..              ..
..             ..              ..


I am trying to cluster these based upon the crime types. 

For example, if in any  region, THEFT has a high frequency of occurrence, based on the data set, it should show up as a cluster. I have tried clustering using the lat-long data only, and that does not seem to have any meaning for this crime dataset. 

I am fairly new to data mining, and gradually figuring my way out. 

How can I cluster the data using the latitude and longitude values based such that the clusters are related to each other through the crime-type? Is there any tool available that can use the lat-long data and cluster them on the crime-type basis? Otherwise, I can even write a script once I understand how this can be done.

Also, has anyone had any previous experience in crime-data-mining? In what other ways can I find interesting patterns from a crime data-set?

Thanks a lot!
There is no meaningful way to combine "type" with a distance in meters.

My suggestion is that you:


split the data set by type.
cluster each type, with DBSCAN, haversine distance, and the same minpts/eps values for each crime type
compare the resulting clusters for similarity and differences