"How do I do clustering on a map correctly" is a common question in mapping applications.
Short answer: Clustering doesn't work. There is no such thing as good clustering UX, as clustering is not good UX.
The reason is simple: The user is either interested in an area, or a point. He might be interested in the density of an area (population, number of ice cream shops within 5 minutes of walk, whatever), but points have no density.
The "average" of two points will be neither of them. Sometimes this is meaningful (the average of the stones of Stonehenge might represent the monument), but most of the time, it does not make any sense.
This perhaps explains why neither Google Maps, Nokia Maps, or any other large-scale map providers don't have clustering on their map. Note: most of them had it in concept phase, Google Maps' even reached A/B test stage when it was dropped.
Of course, like for every UX rule of thumb, there are exceptions. So far I have seen only one: property adverts. It is a meaningful information how many properties are on sale in a given area, albeit the one I saw had the notion of districts and neighborhoods for clustering. (it's http://otthonterkep.hu/ )
The solution is also connected to this: you should decide whether you have areas, or point of interests. Of course, what might be a point from "above" could be an area when looked closely: for example, a city.
An area can have density, and then you can do density coloring of the area. If you don't have defined geographical areas for whatever reason, you might use heatmaps, or cell-based density information (which are essentially artifical areas).
Clustering is essentially density information shown as points - again, with no attached meaning.
Display density based on regions if possible, always show top questions of any view as a list beneath the map, and show individual questions only once zoomed in enough so that they can be safely tapped independently.
Suppose you consider heatmaps, in case you don't have regions.
One of my map products once defined maximal heat of restaurants as any point, from where at least 20 restaurants are within 5 minutes of walking distance, medium heat was 10 restaurants, and no heat was defined for any place where at least 2 restaurants weren't available - so single-standing structures wouldn't cause "heat", as heat was about choice and abundance.
This was zoomlevel-agnostic - a common mistake what heatmap providers do is that they measure in pixels instead of meters, therefore their map tells a different story on each zoomlevel.
Once zoomed in, you've seen places meeting the best 10% criteria (or, to be more precise: the ones who were over the neck of the exponential curve of ratings) highlighted with big icons, while the rest of them were little dots - something like Google Maps does if it still does it for "restaurant" searches.
I hope it answers your questions.
The author works at a map-centered company, had 2 map-based startups, and worked for one of the biggest map providers of the word as a UX Designer/Prototyper at the Map Design department.