Cluster Analysis (in Marketing) - Clustering Procedures

Clustering Procedures

There are several types of clustering methods:

• Non-Hierarchical clustering (also called k-means clustering)
  • first determine a cluster center, then group all objects that are within a certain distance
  • examples:
    • Sequential Threshold method - first determine a cluster center, then group all objects that are within a predetermined threshold from the center - one cluster is created at a time
    • Parallel Threshold method - simultaneously several cluster centers are determined, then objects that are within a predetermined threshold from the centers are grouped
    • Optimizing Partitioning method - first a non-hierarchical procedure is run, then objects are reassigned so as to optimize an overall criterion.
• Hierarchical clustering
  • objects are organized into an hierarchical structure as part of the procedure
  • examples:
    • Divisive clustering - start by treating all objects as if they are part of a single large cluster, then divide the cluster into smaller and smaller clusters
    • Agglomerative clustering - start by treating each object as a separate cluster, then group them into bigger and bigger clusters
      • examples:
        • Centroid methods - clusters are generated that maximize the distance between the centers of clusters (a centroid is the mean value for all the objects in the cluster)
        • Variance methods - clusters are generated that minimize the within-cluster variance
          • example:
            • Ward’s Procedure - clusters are generated that minimize the squared Euclidean distance to the center mean
        • Linkage methods - cluster objects based on the distance between them
          • examples:
            • Single Linkage method - cluster objects based on the minimum distance between them (also called the nearest neighbour rule)
            • Complete Linkage method - cluster objects based on the maximum distance between them (also called the furthest neighbour rule)
            • Average Linkage method - cluster objects based on the average distance between all pairs of objects (one member of the pair must be from a different cluster)