218,312 research outputs found
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 Wardâs 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 Wardâs 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
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
Cluster Analysis using Microgreographic data
In this paper we try to identify manufacturing and service clusters in Spain, using data from Mercantile Registers of 2006. The proposed methodology partially follows contributions of Duranton and Overman (2005), Brenner (2003 and 2004) and Ellison and Glaser (1997), but departing from them we improve such approaches by several ways. In order to sum up, we can detail our approach and divide it into five stages. Firstly, we divide space into homogeneous cells. Secondly, we create industry specific maps departing from firmsĂ¢âââ⢠georeferenced data. Thirdly, we create multiple random industry specific maps under two conditions: i) total number of firms at each industry remains constant and ii) total number of firms at each cell remains constant. Fourthly, we compare the observed spatial distribution of firms with random simulations of such distribution and we check if there is some kind of concentration compared to the random distribution. Fifthly, for each industry we map the areas where the concentration of firms is significantly higher than expected. Previous scheme allows us to identify real clusters (of different shapes and sizes) for all range of manufacturing and service activities and to use this information to design public policies related to such industries. Keywords: cluster analysis, geographic data, microeconomics, regional economics.
Benchmarking in cluster analysis: A white paper
To achieve scientific progress in terms of building a cumulative body of
knowledge, careful attention to benchmarking is of the utmost importance. This
means that proposals of new methods of data pre-processing, new data-analytic
techniques, and new methods of output post-processing, should be extensively
and carefully compared with existing alternatives, and that existing methods
should be subjected to neutral comparison studies. To date, benchmarking and
recommendations for benchmarking have been frequently seen in the context of
supervised learning. Unfortunately, there has been a dearth of guidelines for
benchmarking in an unsupervised setting, with the area of clustering as an
important subdomain. To address this problem, discussion is given to the
theoretical conceptual underpinnings of benchmarking in the field of cluster
analysis by means of simulated as well as empirical data. Subsequently, the
practicalities of how to address benchmarking questions in clustering are dealt
with, and foundational recommendations are made
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