MPCluster for MapPoint Click to buy MPMileage

Analyze your Microsoft MapPoint data for clusters...

Sample cluster identified by MPCluster

Do you need to find natural groups in your MapPoint data?

For example, do you have your customers plotted as pushpins, and need to find natural groups for the best utilization of your salesforce? Or perhaps you need to identify "hotspots" in specific product sales?

If so, MPCluster will solve your problems. MPCluster identifies groups or clusters in your MapPoint datasets. MPCluster can then draw a boundary shape around each cluster and/or mark each cluster's center with a pushpin.

MPCluster lets you set the maximum number of clusters to find. Minimum and maximum size limits can also be set for the clusters.

Benefits include:

Additional features, include:

Click here to download MPCluster. This is a free trial version that will last for 14 days, and must be registered if you wish to use it beyond this period. MPCluster licenses can be purchased online for US$100 each. Other purchase options and volume discounts are also available.

Please use our contact form for MPCluster support.


"We tested the combination of MPCluster, Mappoint 2010, and Excel 2003 as a low-cost, cluster analysis solution with a small learning curve. Our task was to analyze a sample of roughly 1000 German Biotechnology Firms and sort them into clusters; we defined clusters as conglomerates of 5+ firms within a maximum radius of 50 miles.
MPCluster, the commercial geographic clustering plugin for Microsoft Mappoint utilizes the K-means algorythm to find clusters of datasets imported into Mappoint. The cluster boundries, once calculated, can be exported to Excel. After several runs, MPCluster found roughly 30 clusters, most of which made sense. We had to use common sense to remove statistical outliers and group or ungroup certain clusters, but overall MPCluster did a great job of getting results quickly.
While MPCluster and Mappoint together do not replace a professional GIS, they can be used for basic cluster analysis to get results quickly and at a low price point. Also, in our experience, the development team was extremely responsive and professional. We look forward to upcoming versions of MPCluster and recommend anyone interested in cluster analysis to try MPCluster."
Andrew Isaak, Institut for Small Business Research, Mannheim Business School, Germany