Applied Hierarchical Cluster Analysis with Average Linkage Algoritm

Comparison of Hierarchical Cluster Analysis Methods Applied to Image Segmentation by Watershed Merging

Hierarchical Agglomerative Cluster Analysis Applied to WIBS 5-Dimensional Bioaerosol Data Sets

Introduction Primary biological aerosol particle (PBAP) classification requires discrimination of particles various diverse sources which may have wide reaching effects in the atmosphere. In order to predict these effects under future emissions scenarios it is useful to be able to identify ambient PBAP concentration. To date, this has largely been achieved by the use of off-line techniques, whi...

Hierarchical Clustering With Prototypes via Minimax Linkage.

Agglomerative hierarchical clustering is a popular class of methods for understanding the structure of a dataset. The nature of the clustering depends on the choice of linkage-that is, on how one measures the distance between clusters. In this article we investigate minimax linkage, a recently introduced but little-studied linkage. Minimax linkage is unique in naturally associating a prototype ...

Emotion Annotation Using Hierarchical Aligned Cluster Analysis

The correctness of annotation is quite important in supervised learning, especially in electroencephalography(EEG)-based emotion recognition. The conventional EEG annotations for emotion recognition are based on the feedback like questionnaires about emotion elicitation from subjects. However, these methods are subjective and divorced from experiment data, which lead to inaccurate annotations. ...

Using hierarchical cluster analysis in nursing research.

This article is a pedagogical piece on hierarchical cluster analysis, a method for investigating the structure underlying data. Such methods are useful for finding similar groups of cases in data sets when it is not known a priori how many groups are present. The article is laid out asfollows: First, a brief history and overview of the methods is presented; second, an illustrative example with ...