In this paper, we improve our previous cluster model selection method for agglomerative hierarchical speaker clustering (AHSC) based on incremental Gaussian mixture models (iGMMs). In the previous work, we measured the likelihood of all the data points in a given cluster for each mixture component of the GMM modeling the cluster. Then, we selected the N -best component Gaussians with the highes...

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 ...

We present a slicing-based coherence measure for clusters of DTI integral curves. For a given cluster, we probe samples from the cluster by slicing it with a plane at regularly spaced locations parametrized by curve arc lengths. Then we compute a stability measure based on the spatial relations between the projections of the curve points in individual slices and their change across the slices. ...

q-mode hierarchical cluster (hca) and principal component analysis (pca) were simultaneously applied to groundwater hydrochemical data from the three times in 2004: june, september, and december, along the ain azel aquifer, algeria, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the h...

In this paper, we discuss interactively visualizing hierarchical clustering using multidimensional scaling (MDS) and the minimal spanning tree (MST). We can examine the sequential process leading to agglomerative or divisive hierarchical clustering, compare the di erent agglomerative methods, and detect in uential observations better than is possible with dendrograms.

In the hierarchical wireless sensor network (WSN), selecting cluster head (CH) is important issue to increase the network energy efficiency, scalability and lifetime. For the sake of balancing energy expenditure of sensor nodes and improving the performance of routing, We propose a distributed and self-adaptive cluster-head selection algorithm. Based on the hierarchical agglomerative clustering...

In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance...

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...