Figure 2: An result of experiment I. Figure 3: An result of experiment II. Branching Competitive Learning For Data Clustering. Irwin King, Ada Fu and Laiwan Chan.
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Figure 2: An result of experiment I
Figure 3: An result of experiment II
Irwin King, Ada Fu and Laiwan Chan
This project proposes a novel modification to the classical Competitive Learning (CL) by adding a dynamic branching mechanism to neutral networks so that the number of neurons can be increased over time until the networks reaches a good estimation of the cluster number in a data set.
The Branching Criterion
1. The angle criteria based on the angle between
current moving direction and the previous moving
direction of a synaptic vector:
2. The distance criteria based on the distance between
the input data and the winner:
where are an randomly selected data at
current step, the winner in current competition, angle
and distance threshold.
The Algorithm of BCL
1. Initialize the first synaptic vector.
2. Randomly take a sample from the
dataset, find the winner of the current
competition in the set of synaptic vector
where is the frequency that wins the
competition up to now.
3. If satisfies the branching criterion above, a new
neuron is spawn off from
otherwise, update by
An Illustration of BCL
Figure 1: An illustration of the procedure of the BCL
(1) Initialization of the first synaptic vector.
(2) Branching points of synaptic vectors.
(3) Final convergence of synaptic vectors.
Table 1: Results of experiment III