The houses might be noteworthy because of their size, their historical interest, or their current market value. Clustering algorithms exist to assign each point to exactly one cluster, with the number of clusters being defined by the user. Clustering is a technique that is quite useful in spatial data mining applications. This requires a suitable similarity function.
Euclidean distance is a natural choice of similarity function for spatial data mining.
One area of our research identifies spatial relationships between clusters and features. The objective is to identify likely relationships. The information is ranked, so that qualitative information is presented first. This problem is not trivial. There may be tens of thousands of features to consider and the input features can vary significantly from user to user.