Machine learning

1. Hierarchical Approach
DIANA

2. DIANA- Explored
• In DIANA, a divisive hierarchical clustering method, all of the objects
form one cluster.
• The cluster is split according to some principle, such as the minimum
Euclidean distance between the closest neighboring objects in the
cluster.
• The cluster splitting process repeats until, eventually, each new
cluster contains a single object or a termination condition is met.

3. Hierarchical Clustering
• Use distance matrix as clustering criteria. This method does
not require the number of clusters k as an input, but needs a
termination condition
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(DIANA)

4. DIANA (Divisive Analysis)
• Inverse order of AGNES
• Start with root cluster containing all objects
• Recursively divide into subclusters
• Eventually each cluster contains a single object
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5. Overview
• Divisive Clustering starts by placing all objects into a single group. Before we start
the procedure, we need to decide on a threshold distance.
The procedure is as follows:
1. The distance between all pairs of objects within the same group is determined and
the pair with the largest distance is selected.
2. This maximum distance is compared to the threshold distance.
• If it is larger than the threshold, this group is divided in two. This is done by
placing the selected pair into different groups and using them as seed points. All
other objects in this group are examined, and are placed into the new group
with the closest seed point. The procedure then returns to Step 1.
• If the distance between the selected objects is less than the threshold, the
divisive clustering stops.

6. Example