Spatial Data Mining
My PechaKucha talk at DataBeers : “Visualizing Geolocated Tweets: a Spatial Data Mining Approach”.
Category: Artificial Intelligence
Post-processing OPTICSxi Clustering
On a previous post, I expressed my concerns regarding the results of OPTICSxi clustering. Namely, I mentioned an “annoying” spike effect, that turns out massively almost at any simulation (so massively that it is almost a “feature”). A post in … Continue reading →
Parametrizing and interpreting OPTICSxi Clustering
For a while now, I have been working on the application of the OPTICS clustering, for user generated data in cities. OPTICS is a density-based algorithm that attempts to overcome some of the “weaknesses” of its most famous counterpart: DBSCAN. … Continue reading →
Clustering Geospatial Data
Recently I have been looking into different algorithms for the clustering geospatial data. The problem of finding “similar” regions in space, is a very interesting one, since this type of classification enables a whole range of applications (e.g.: urban development, … Continue reading →