Assessing the risk and returns of multiple firms can be difficult; grouping more than two hundred thirty equities in the Philippine stock market is nearly possible with the help of the clustering technique. Cluster analysis can help by aggregating returns and risk so the investor or trader can co...
% Done with input argument processing, begin clustering%dispfmt = '%6dt%6dt%8dt%12g';D = repmat(NaN,n,k); % point-to-cluster distancesDel = repmat(NaN,n,k); % reassignment criterionm = zeros(k,1);totsumDBest = Inf;for rep = 1:repsswitch start...
It is structured through the combination of K-means clustering based on the Pearson index and the multi-head attention mechanism, which are, respectively, described in Section 2.2 and Section 2.3. 2.1. The PKMA Model Beginning with the inputs of the model, the implementation of the PKMA is...
For homogeneous grouping, a cluster analysis with two widely used non-hierarchical grouping methods was used, namely the K-Mean and K-Medoid methods. The clustering procedures can be hierarchical (tree-like structure) or non-hierarchical. The hierarchical method forms clusters gradually, and ...