Cluster Analysis (Contd.) Group C

Gaurav Gupta

14075

1) Nodal Method

In this method one selects a point or points that will serve as nodes for forming clusters or focal points. The rest of the points are then allocated to each cluster on the basis of their distance from the focal point

·         Choose as nodes those objects (points) that have the greatest distance between them.

·         Consider these two objects as focal nodes and allocate all the remaining objects to one cluster or the other based on their distance from the polar nodes.

·         Split the resulting two clusters in the same way. Continue this process till the collection of points is split into its original members.

2) Factor Analysis Method

Another way of developing clusters is by using the method called as Q-factor analysis. By this method one can determine which objects logically belong together: This is also called as inverse factor analysis.

3) Linkage Method

There are three methods in this Single Linkage Method, Complete Linkage Method and Average Linkage method.

The method starts by finding out the points with shortest Euclidean distance. In the next stage, depending upon the cut-off distance, objects are associated to the clusters. The complete linkage option starts out in the same manner by clustering the two closest points. However, the distance between two clusters is the longest distance from a point in the first cluster to a point in the second cluster. In the average linkage option, the distance between the two clusters is the average distance from points in the first cluster to points in the second cluster.

The following aspects should be kept in mind while using cluster analysis method

·         A number of clusters may emerge after doing analysis. However , there is limit to the number of cluster that a company can consider due to

−        Limitation of market potential within a cluster

−        Difference between clusters not sharply defined

·         Cluster analysis provides a way of segmenting the market but these segments are not water tight compartments. Products that are developed for a particular segment may attract people from other segments too.

·         The characteristics of a cluster may change over time, as the consumers’ economic status, education, lifestyle, etc., change over time then the company has to take a relook at the market place.

·         The clusters that have been identified are used developing further marketing strategies in the areas " like 'product developments, advertising research, distribution strategies, pricing strategies etc,

·         The most important assumption in cluster analysis is that the basic measure of similarity on which clustering is based is a valid measure ^'of the similarity between objects. A second major assumption is that there is theoretical justification and basis for structuring objects into clusters. As with other multivariate techniques, there should be theory and logic underlying the cluster analysis.

·         The major limitation of cluster analysis is the difficulty in evaluating the quality of the clusters. It is very, difficult to know exactly which clusters are very similar and which objects are dissimilar, and also difficult to select clustering criterion.