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I'm looking into clustering points on a map (latitude/longitude). Are there any recommendations as to a suitable algorithm that is fast and scalable?
More specifically, I have a series of latitude/longitude coordinates and a map viewport. I'm trying to cluster the points that are close together in order to remove clutter.
I already have a solution to the problem (see here), only I am wondering if there is any formal algorithm that solves the problem efficiently.
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Clustering technique is used in various applications such as market research and customer segmentation, biological data and medical imaging, search result clustering, recommendation engine, pattern recognition, social network analysis, image processing, etc.
A cluster or map combines the two stages of brainstorming (recording ideas and then grouping them) into one. It also allows you to see, at a glance, the aspects of the subject about which you have the most to say, so it can help you choose how to focus a broad subject for writing.
Each cluster is represented by a circle with a diameter that is directly proportional to the number of markers it represents which, in turn, is also numerically represented. Clicking on a cluster will show you the individual markers or sub-clusters.
The DBSCAN is better than other cluster algorithms because it does not require a pre-set number of clusters. It identifies outliers as noise, unlike the Mean-Shift method that forces such points into the cluster in spite of having different characteristics. It finds arbitrarily shaped and sized clusters quite well.
For a virtual earth application I've used the clustering described here. It's lightning fast and easily extensible.
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