USING THEMATIC CLUSTERS
 

 

 

 

 

 

 

 

 

 

 

 

 

 

The goal of the clustering project was to validate editorial zones that already existed. Each editorial zone consisted of a set of towns assigned one of the four clusters described above. The next step was to manually increase each zone’s purity by swapping towns with adjacent zones. In the neighboring West 1 zone, all the towns are in Cluster 2 except for Waltham and Watertown which are in Cluster 1B. Swapping Brook­ line into West 1 and Watertown and Waltham into City would make it possible for both editorial zones to be pure in the sense that all the towns in each zone would share the same cluster assignment. The new West 1 would be all Cluster 2, and the new City would be all Cluster 1B.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Automatic cluster detection is an undirected data mining technique that can be used to learn about the structure of complex databases. By breaking com­ plex datasets into simpler clusters, automatic clustering can be used to improve the performance of more directed techniques. By choosing different distance measures, automatic clustering can be applied to almost any kind of data. It is as easy to find clusters in collections of news stories or insurance claims as in astronomical or financial data. Clustering algorithms rely on a similarity metric of some kind to indicate whether two records are close or distant. Often, a geometric interpretation of distance is used, but there are other possibilities, some of which are more appropriate when the records to be clustered contain non-numeric data. One of the most popular algorithms for automatic cluster detection is K-means. The K-means algorithm is an iterative approach to finding K clusters based on distance. The chapter also introduced several other clustering algorithms. Gaussian mixture models, are a variation on the K-means idea that allows for overlapping clusters. Divisive clustering builds a tree of clusters by successively dividing an initial large cluster. Agglomerative clustering starts with many small clusters and gradually combines them until there is only one cluster left. Divisive and agglomerative approaches allow the data miner to use external criteria to decide which level of the resulting cluster tree is most useful for a particular application.