scholarly journals Clusteranalysis as a swine farm qualifying method

2007 ◽  
pp. 165-174
Author(s):  
Sándor Kovács ◽  
Péter Balogh
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Theoretical Background ◽  
Distance Measures ◽  
Swine Farm ◽  
Hierarchical Method

Cluster Analysis is one of the most favorite multivariable statistical methods, which is actually a special type of aggregating method. Observations are clustered by variables belonged to the observations. Our purpose is to create such clusters, in which the elements are the most similar, and between the clusters they are the most variant. For example these clusters could be the qualitative classifications of farms.There have been several methods in Cluster Analysis as well as numerous distance measures, which could be used. In this article, we study all of these methods and measures. After we show the theoretical background, we apply the method in a given casestudy to control the qualitative classifications of experts. In this study, we use both the hierarchical and the non-hierarchical method, and also compare them. We would like to attract the attention that the most important problem of the analysis is to determine the optimal of clusters.

The Application of Mathematical Statistics in Urban Geography Research

2014 ◽  
Vol 919-921 ◽  
pp. 1630-1633
Author(s):  
Xiu Feng Ma
Keyword(s):  
Cluster Analysis ◽  
Regression Analysis ◽  
20Th Century ◽  
Statistical Methods ◽  
Urban Geography ◽  
Mathematical Statistics ◽  
Multivariate Cluster Analysis ◽  
Important Position ◽  
Multivariate Cluster

In the middle of the 20th century, geography has experienced a number "revolution". statistical occupies more and more important position in the urban geography research. This paper analyzes the application of statistics in the development of the urban geography. And with the method of case study, illustrates the multivariate cluster analysis, regression analysis and other statistical methods in the study of urban geography specific applications

scholarly journals Study of anaesthetical activity of alkoxyphenylcarbamic acid esters using statistical methods

2022 ◽  
Vol 7 (1) ◽  
pp. 123-128
Author(s):  
Tatiana Ďurčeková ◽  
Ján Mocák ◽  
Jozef Lehotay ◽  
Jozef Čižmárik
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Statistical Methods ◽  
Molar Mass ◽  
Principal Component ◽  
Retention Factor ◽  
Factor Log ◽  
And Cluster Analysis ◽  
Derivatives Of ◽  
Acid Esters

Anaesthetical activity of 113 morpholinoethyl-, piperidinoethyl-, piperidinopropyl- and azepanoethyl- ester derivatives of alkoxyphenylcarbamic acid was characterized by several chemometrical techniques. The surface anaesthetical activity, A, and the infiltration anaesthetical activity, B, were correlated to lipophilicity, (expressed by the logarithm of the HPLC retention factor, log k), the length of the side alkoxy chain (represented by the number n of carbon atoms), molar mass M as well as the ester type. Principal component analysis and cluster analysis were used for predicting both types of the anaesthetic activity of the alkoxyphenylcarbamic acid esters.

scholarly journals The Exploitation of Distance Distributions for Clustering

2021 ◽  
Vol 20 (03) ◽  
Author(s):  
Michael C. Thrun
Keyword(s):  
Cluster Analysis ◽  
Distance Measure ◽  
Machine Learning Algorithms ◽  
Distance Measures ◽  
Distribution Analysis ◽  
Distance Distributions ◽  
Evaluation Phase ◽  
Unsupervised Methods ◽  
Error Probabilities ◽  
Data Context

Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised methods based on error probabilities, implicitly setting the goal of reproducing predefined partitions in data. Such studies use clusters of data that are often based on the context of the data as well as the custom goal of the specific study. Depending on the data context, different properties for distance distributions are judged to be relevant for appropriate distance selection. However, if cluster analysis is based on the task of finding similar partitions of data, then the intrapartition distances should be smaller than the interpartition distances. By systematically investigating this specification using distribution analysis through the mirrored-density (MD plot), it is shown that multimodal distance distributions are preferable in cluster analysis. As a consequence, it is advantageous to model distance distributions with Gaussian mixtures prior to the evaluation phase of unsupervised methods. Experiments are performed on several artificial datasets and natural datasets for the task of clustering.

scholarly journals PENGELOMPOKAN DESA/KELURAHAN DI KOTA DENPASAR MENURUT INDIKATOR PENDIDIKAN

2016 ◽  
Vol 5 (2) ◽  
pp. 38
Author(s):  
NI WAYAN ARIS APRILIA A.P ◽  
I GUSTI AYU MADE SRINADI ◽  
KARTIKA SARI
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
The Other ◽  
Single Linkage ◽  
Complete Linkage ◽  
Hierarchical Method ◽  
Average Linkage ◽  
Different Characteristics

Cluster analysis is one of data analysis used to classify objects in clusters which has objects with the same characteristics, whereas the other cluster has different characteristics. One part of the method of analysis cluster is hierarchy method. In a hierarchical method there are methods of linkage in the form of incorporation. Generally, methods of linkage is divided into 5 methods: single linkage, complete linkage, average linkage, Ward and centroid.  The purpose of this study was to determine the best method of linkage among the method of single linkage, complete linkage, average linkage, and Ward, using Euclidean and Pearson proximity distance. Base on the smallest value of CTM (Cluster Tightness Measure), the best method of linkage as a result of this research was average linkage in Pearson distance.

scholarly journals An Analysis of Seasonal Waste Draining for the Urban Agglomeration Using Statistical Methods

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 976 ◽  
Author(s):  
Dariusz Młyński ◽  
Karolina Kurek ◽  
Piotr Bugajski
Keyword(s):  
Cluster Analysis ◽  
Spectral Analysis ◽  
Environmental Protection ◽  
Statistical Methods ◽  
Treatment Plant ◽  
Urban Agglomeration ◽  
Statistical Techniques ◽  
Practical Solution ◽  
Observational Series ◽  
Biotic Environment

The aim of the work was to analyze the seasonality of the sewage outflow from the urban agglomeration of Radom, using statistical methods in the aspect of environmental protection. The research was carried out on the basis of the observational series covering the daily volume of the sewage outflow in the years 2013–2015. The assessment was carried out according to the following stages: identification of the distribution of the average daily sewage outflow in particular months using nuclear estimators, seasonal evaluation using the Colwell indicators and Fourier spectral analysis, and identification of homogeneous seasons with regard to the sewage outflow using the cluster analysis. On the basis of the calculations that were carried out, no significant seasonality of the sewage outflow was noted, which results from the separated character of the sewage system in the urban agglomeration of Radom. The analyses showed that the applied statistical techniques are a practical solution for identifying the seasonality of sewage inflow to the treatment plant, thanks to which it will be possible to take appropriate actions related to minimizing the harmful impact of hydraulic overload on the biotic environment of the natural receiver.

scholarly journals How the calving order of cows affects the performance of Nellore calves

2017 ◽  
Vol 40 ◽  
pp. 34519 ◽  
Author(s):  
Rafael Kill Silveira ◽  
Marcelo Jangarelli
Keyword(s):  
Cluster Analysis ◽  
Average Daily Gain ◽  
Male And Female ◽  
Complete Linkage ◽  
Similar Performance ◽  
Hierarchical Method ◽  
Multivariate Cluster Analysis ◽  
Multivariate Cluster ◽  
Daily Gain ◽  
Effect Of Age

This study aimed to verify the effect of age of dam on the performance of male and female Nellore calves, using the following variables: average daily gain (ADG), adjusted weight for 205 days of age (W205), and number of days to reach 160 kg (D160). Information were collected from a commercial herd consisting of 1,122 calves and 1,009 heifers and their mothers. To classify animals with similar performance based on the cows’ calving orders (age of dam), the multivariate cluster analysis was adopted through the complete linkage hierarchical method. The best performance was observed in the calves of cows in their sixth calving at most; for heifers, the best performance was seen in those born to cows in their eighth calving at most. Cows in their eighth calving should be discarded. 

scholarly journals Application of multivariate statistical methods in the assessment of mountain organic soil transformation in the central Sudetes

2017 ◽  
Vol 54 (1) ◽  
pp. 43-59
Author(s):  
Bogna Zawieja ◽  
Bartłomiej Glina
Keyword(s):  
Cluster Analysis ◽  
Statistical Methods ◽  
Science Research ◽  
Organic Soil ◽  
Soil Science ◽  
Organic Soils ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical ◽  
Discriminant Variable ◽  
And Cluster Analysis

Summary In studies of organic soil degradation and transformation, alongside the conventional methods used in soil science, an increase in the importance of advanced statistical methods can be observed. In this study some multivariate statistical methods were applied in an investigation of organic soil transformation in the central Sudetes. Andrews curves, linear and kernel discriminant variable analysis and cluster analysis were used. The similarities among peatland soils and their layers were determined. It can be stated that the application of statistical methods in soil science research related to organic soil transformation is a valuable tool. The use of various statistical methods (such as Andrews curves, linear and kernel discriminant variables and cluster analysis) can with high probability confirm earlier laboratory or field observations. This is particularly justified in the case of organic soils derived from varied geobotanical peat materials, different types of peatlands and water supply types, which impact the primary properties of the soil.

Sign in / Sign up

Export Citation Format

Share Document