Clustering algorithms are routinely used in biomedical disciplines, and are a basic tool in bioinformatics. Depending on the task at hand, there are two most popular options, the central partitional techniques and the Agglomerative Hierarchical Clustering techniques and their derivatives. These methods are well studied and well established. However, both categories have some drawbacks related t...

Over 3 000 Mozambican smallholder farmers supply cassava to Company XYZ. XYZ needs an effective supplier segmentation method gain insight into how it should direct its resources for the greatest impact. This paper describes application of the  k -means algorithm, agglomerative hierarchical clustering, and a self-organising map with ward clustering segment these suppliers. The insights gained fr...

INTRODUCTION The aim of this study was to identify subsets of patients with fibromyalgia with similar symptom profiles using the Outcome Measures in Rheumatology (OMERACT) core symptom domains. METHODS Female patients with a diagnosis of fibromyalgia and currently meeting fibromyalgia research survey criteria completed the Brief Pain Inventory, the 30-item Profile of Mood States, the Medical ...

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...

This dissertation explores methods for cluster analysis of acoustic data. Techniques developed are applied primarily to whale song, but the task is treated in as general a manner as possible. Three algorithms are presented, all built around hidden Markov models, respectively implementing partitional, agglomerative, and divisive clustering. Topology optimization through Bayesian model selection ...

A distributed memory parallel version of the group average Hierarchical Agglomerative Clustering algorithm is proposed to enable scaling the document clustering problem to large collections. Using standard message passing operations reduces interprocess communication while maintaining efficient load balancing. In a series of experiments using a subset of a standard TREC test collection, our par...

Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. Objects in the same cluster should be as similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in the other clusters. Document clustering has become an increasingly important task in analysing huge documents. The challenging aspect to analyse ...