Hierarchical Gaussian Mixture Model with Objects Attached to Terminal and Non-terminal Dendrogram Nodes
This is an incremental improvement for clustering applications, offering better noise modeling and dendrogram compactness.
The paper tackles hierarchical clustering by introducing a Gaussian mixture model that stores objects in both terminal and non-terminal nodes, which helps in noise detection and generates more compact dendrograms with higher F-measure quality compared to regular hierarchical mixture models.
A hierarchical clustering algorithm based on Gaussian mixture model is presented. The key difference to regular hierarchical mixture models is the ability to store objects in both terminal and nonterminal nodes. Upper levels of the hierarchy contain sparsely distributed objects, while lower levels contain densely represented ones. As it was shown by experiments, this ability helps in noise detection (modelling). Furthermore, compared to regular hierarchical mixture model, the presented method generates more compact dendrograms with higher quality measured by adopted F-measure.