Parametric model-based clustering
Parametric, model-based algorithms learn generative models from the data, with each model corresponding to one particular cluster. Accordingly, the model-based partitional algorithm will select the most suitable model for any data object (Clustering step}, and will recompute parametric models using data specifically from the corresponding clusters {Maximization step). This Clustering-Maximization framework have been widely used and have shown promising results in many applications including complex variable-length data. The paper proposes (Experience-Innovation} (EI) method as a natural extension of the (Clustering-Maximization} framework. This method includes 3 components: (1) keep the best past experience and make empirical likelihood trajectory monotonical as a result; (2) find a new model as a function of existing models so that the corresponding cluster will split existing clusters with bigger number of elements and smaller uniformity; (3) heuristical innovations, for example, several trials with random initial settings. Also, we introduce clustering regularisation based on the balanced complex of two conditions: (1) significance of any particular cluster; (2) difference between any 2 clusters. We illustrate effectiveness of the proposed methods using first-order Markov model in application to the large web-traffic dataset. The aim of the experiment is to explain and understand the way people interact with web sites.