MATLAB Code for Calculating KL Divergence in Data Mining Applications

In data mining applications, calculating KL divergence serves as a fundamental method for quantifying the difference between two probability distributions. The MATLAB implementation below demonstrates how to compute this metric effectively:

% Data initialization and preprocessing data1 = [1 2 3 4 5]; % First probability distribution vector data2 = [0.5 1.5 2.5 3.5 4.5]; % Second probability distribution vector % KL divergence computation using element-wise operations kl_distance = sum(data1 .* log(data1 ./ data2)); % The algorithm implements the standard KL formula: Σ P(i) * log(P(i)/Q(i)) % Key functions: element-wise multiplication (.*) and division (./) with logarithmic transformation % Result output and validation disp(kl_distance); % Displays computed divergence value in command window

This code initializes two distribution vectors, computes their KL divergence using the mathematical formula Σ P(x) log(P(x)/Q(x)), and outputs the result to the command window. The implementation handles element-wise operations efficiently and provides a straightforward approach for distribution comparison in MATLAB environment.