The major tasks of clustering evaluation include the following:
- Before you start: Assess clustering tendency. Assess whether a non-random structure exists in the data. Clustering analysis on a data set is meaningful only when there is a non-random structure in the data.
- Next: Determine the number of clusters in a dataset. How many clusters are there to find? Like k-means, many methods require the number of clusters in advance as a parameter to the method.
- After clustering: Measure the clustering quality. There are various quality measures according to different criteria.
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