Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 4
Top-Down Approach
The top-down clustering method is also called the divisive hierarchical
clustering. It the reverse of bottom-up clustering. It starts with a single
cluster consisting of all input objects. After each iteration, it splits the
cluster into two parts having the maximum distance.
Algorithm
Input: I = {I1, I2, … , In}
Output: O
D ← ∞;
K ← 1;
S ← {I 1 ,I 2 , ... , I n };
O ← <D, K, S >;
repeat
X ← containing two data objects with the longest
distance dist;
Y ← ∅;
S ← S – X;
X i ← data object in A with maximum − D(X i , X);
X ← X − {X i };
Y ← Y ∪ {X i };
repeat
forall data object X j in Xdo
e(j) ← − D(X j , X) − − D(X j , Y);
end for
if∃e(j) > 0 then
X k ← data object in X with maximum e(j);
X ← X − {X k };
Y ← Y ∪ {X k };
split ← TRUTH;
else
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Unsupervised Learning: Clustering