Basic Procedure
- Formulate the problem - select the variables to which you wish to apply the clustering technique
- Select a distance measure - various ways of computing distance:
- Squared Euclidean distance - the sum of the squared differences in value for each variable
- Manhattan distance - the sum of the absolute differences in value for any variable
- Chebyshev distance - the maximum absolute difference in values for any variable
- Mahalanobis (or correlation) distance - this measure uses the correlation coefficients between the observations and uses that as a measure to cluster them. This is an important measure since it is unit invariant (can figuratively compare apples to oranges)
- Select a clustering procedure (see below)
- Decide on the number of clusters
- Map and interpret clusters - draw conclusions - illustrative techniques like perceptual maps, icicle plots, and dendrograms are useful
- Assess reliability and validity - various methods:
- repeat analysis but use different distance measure
- repeat analysis but use different clustering technique
- split the data randomly into two halves and analyze each part separately
- repeat analysis several times, deleting one variable each time
- repeat analysis several times, using a different order each time
Read more about this topic: Cluster Analysis (in Marketing)
Famous quotes containing the word basic:
“Not many appreciate the ultimate power and potential usefulness of basic knowledge accumulated by obscure, unseen investigators who, in a lifetime of intensive study, may never see any practical use for their findings but who go on seeking answers to the unknown without thought of financial or practical gain.”
—Eugenie Clark (b. 1922)