Association and sequencing. Clustering is a descriptive technique that groups similar entities together and puts dissimilar entities in different groups. It can be used in marketing for finding customer affinity groups, and in health care to find patients with similar profiles. Clustering techniques include a special type of neural net called a Kohonen net, as well as k-means and demographic algorithms. These techniques are not discussed in any of the articles in this issue.
Clustering is very subjective.
Because you must employ a distance measure, like the nearest neighbor technique, the clusters are completely dependent on the distance measure that you used. As a result, two different data miners, working with the same data, could find two completely different ways of clustering that data. And ten data miners could find ten different clusters. Therefore, clustering always requires significant involvement from a business or domain expert who needs to both propose an appropriate distance measure to judge whether the clusters are useful. Because a data mining or knowledge discovery exercise frequently needs both a classification and a clustering algorithm, say, or regression and association, many data mining products include a suite of tools.