Segmentation based on modified Gath-Geva algorithm ppcamod.m



This function contains the modified fuzzy clustering algorithm for multivariate time-series segmentation based on Probabilistic Principal Component Analysis. The algorithm runs niterations number of iterations or until it reaches the stopping criteria (see the value of min_var that is equal to 10-4 by default).

The structure results contains the results:

results.W : weight matrices;

results.S : covariance matrices;

results.C : distance norms; : cluster centers in the feature space;

results.mut : cluster centers in time;

results.szoras : variance in the feature space;

results.szorast: variance in time;

results.pc : prior probabilities of clusters (p(ηi));

results.px : p(xk | ηi) probabilities; : p(tk | ηi) probabilities;

results.pp : p(zk | ηi) probabilities;

results.t : time (equal to input parameter t);

results.x : variables (equal to input parameter x).

The function continuously depicts the results in Figure 5 during it runs.