Cluster Analysis of Ranunculus Species

The aim of the experiment was to examine whether the morphological characters of eleven species of Ranunculus collected from a number of populations were in agreement with the genetic data (isozyme). The method used in this study was polyacrilamide gel electrophoresis using peroxides, estarase, malate dehydrogenase, and acid phosphatase enzymes. The results showed that cluster analysis based on isozyme data have given a good support to classification of eleven species based on morphological groups. This study concluded that in certain species each morphological variation was profit to be genetically based. Key Words: Ranunculus, isozym

An interest rates cluster analysis

An empirical analysis of interest rates in money and capital markets is performed. We investigate a set of 34 different weekly interest rate time series during a time period of 16 years between 1982 and 1997. Our study is focused on the collective behavior of the stochastic fluctuations of these time-series which is investigated by using a clustering linkage procedure. Without any a priori assumption, we individuate a meaningful separation in 6 main clusters organized in a hierarchical structure.Comment: 7 pages, 7 figure

Understanding stakeholder values using cluster analysis

The K-Means and Ward’s Clustering procedures were used to categorize value similarities among respondents of a public land management survey. The clustering procedures resulted in two respondent groupings: an anthropocentrically focused group and an ecocentrically focused group. While previous studies have suggested that anthropocentric and ecocentric groups are very different, this study revealed many similarities. Similarities between groups included a strong feeling towards public land and national forest existence as well as the importance of considering both current and future generations when making management decisions for public land. It is recommended that land managers take these similarities into account when making management decisions. It is important to note that using the Ward’s procedure for clustering produced more consistent groupings than the K-Means procedure and is therefore recommended when clustering survey data. K-Means only showed consistency with datasets of over 500 observations

Halo Model Analysis of Cluster Statistics

We use the halo model formalism to provide expressions for cluster abundances and bias, as well as estimates for the correlation matrix between these observables. Off-diagonal elements due to scatter in the mass tracer scaling with mass are included, as are observational effects such as biases/scatter in the data, detection rates (completeness), and false detections (purity). We apply the formalism to a hypothetical volume limited optical survey where the cluster mass tracer is chosen to be the number of member galaxies assigned to a cluster. Such a survey can strongly constrain $\sigma_8$ ($\Delta\sigma_8\approx 0.05$), the power law index $\alpha$ where $= 1+(m/M_1)^\alpha$ ($\Delta\alpha\approx0.03$), and perhaps even the Hubble parameter ($\Delta h\approx 0.07$). We find cluster abundances and bias not well suited for constraining $\Omega_m$ or the amplitude $M_1$. We also find that without bias information $\sigma_8$ and $\alpha$ are degenerate, implying constraints on the former are strongly dependent on priors used for the latter and vice-versa. The degeneracy stems from an intrinsic scaling relation of the halo mass function, and hence it should be present regardless of the mass tracer used in the survey.Comment: 27 pages, 11 figures, references adde

A robust method for cluster analysis

Let there be given a contaminated list of n R^d-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n-r outliers for the parameters of the g populations; it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.Comment: Published at http://dx.doi.org/10.1214/009053604000000940 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Colour cluster analysis for pigment identification

This paper presents image processing algorithms designed to analyse the colour CIE Lab histogram of high resolution images of paintings. Three algorithms are illustrated which attempt to identify colour clusters, cluster shapes due to shading and finally to identify pigments. Using the image collection and pigment list of the National Gallery London large numbers of images within a restricted period have been classified with a variety of algorithms. The image descriptors produced were also used with suitable comparison metrics to obtain content-based retrieval of the images