scholarly journals Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods

Author(s):  
Jianwei Bu ◽  
Wei Liu ◽  
Zhao Pan ◽  
Kang Ling

Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Noor Rashidah Rashid

Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis test. The result of the comparison used for segmenting tourists of Kapas Island. The statistical software SPSS has been applied to analyze data of this research. The result from Kruskal-Wallis test shows Complete Linkage is more useful in identifying tourists segments. Keywords : Agglomerative Hierarchical Cluster Analysis, Single Linkage, Complete Linkage, Kruskal-Wallis test, tourists


Author(s):  
Edward Slingerland

This chapter argues that, now that we have the texts of our traditions in fully searchable, digitized form, we can begin to read them in new ways. Basic quantitative textual analysis methods are introduced, as well as more sophisticated methods such as word collocation, hierarchical cluster analysis, and topic modeling. The use of online databases to share scholarly knowledge is also explored. Although digital humanities techniques have thus far been of only marginal use, their potential is huge, and they can provide entirely new and important perspectives on our corpora. Quantitative textual analysis of the early Chinese corpus confirms and deepens the conclusion from qualitative analysis that the early Chinese were mind-body dualists.


2016 ◽  
Vol 5 (2) ◽  
pp. 38
Author(s):  
NI WAYAN ARIS APRILIA A.P ◽  
I GUSTI AYU MADE SRINADI ◽  
KARTIKA SARI

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.


Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4866-4866
Author(s):  
Akos Czibere ◽  
Christian Prall ◽  
Ulrich Steidl ◽  
Ingmar Bruns ◽  
Andrea Kuendgen ◽  
...  

Abstract The biology of myelodysplastic syndromes (MDS) is reflected in aberrant transcription levels of the myeloid lineage. It was the purpose of this study to provide potential novel therapeutic targets by identifying molecular alterations that are pertinent to myelodysplastic syndromes. cDNA from bone marrow-derived CD34+ hematopoietic stem and progenitor cells from 6 healthy persons, and 16 patients with MDS (Table 1) was hybridized to cDNA arrays comprising 1185 well-characterized genes. We used the complete cDNA array expression values to build a dendrogram including all samples under investigation. Interestingly, patient survival had a striking impact on our gene signature for it was the parameter that had the strongest statistical association with the segregation of our MDS patients into 2 sub clusters (Figure 1). We examined whether this segregation of MDS patients was associated with age, gender, MDS-type according to FAB, WHO, IPSS, karyotype or survival. We determined correlation coefficients and found “survival” to be the parameter with the strongest statistically significant association with the segregation of MDS patients into 2 sub clusters (Spearman = 0.697, P = 0.003). The WHO type was less significantly associated (Spearman = 0.537, P = 0.032), and the FAB type did not reach statistical significance (Spearman = 0.492, P = 0.053). Transcriptional changes that are associated with short survival may also indicate to potential novel therapeutic targets. Since median age of patients with MDS is 70 at diagnosis, the majority of patients are not suitable for therapies that are not well tolerated. Moreover, some patients have a nearly normal life expectancy. Ideally, new therapeutic agents should, therefore, be tailored for patients with short survival and be well-tolerated like e.g. Bevacizumab and Cetuximab. But, we could not find increased expression of Bevacizumab and Cetuximab related EGFR- or VEGFR-target genes in CD34+ cells from MDS patients with short survival. In sum, there are transcriptional alterations that are strongly associated with short survival, and thus valuable for prognostication of patients with MDS. Table 1. Classification, and karyotypes of patients with MDS who were analyzed by means of cDNA array analysis Patient No. Age/ Sex FAB (a) IPSS (b) WHO (c) Karyotype (a) FAB, French-American-British cooperative group classification, (b) IPSS, international prognostic scoring system; int, intermediate, (c) WHO, World Health Organization classification 1 61/ F RA int-I RCMD 46, XX, 13q- 2 68/ F RA int-I 5q- 46, XX, 5q-, 12p- 3 62/ M RA int-I RCMD 47, XY, +8 4 50/ M RA int-I RCMD 46, XY, t(X;1) 5 18/ F RA low PRA 46, XX 6 51/ M RA low RCMD 46, XY 7 51 /F RA int-I 5q- 46, XX, 5q-, 12p 8 75/ M RAEB high RAEB II 46, XY, 7q- 9 79/ M RAEB-t high sAML complex 10 68/ M RAEB int-II RAEB-II 46, XY 11 70/ F RAEB-t high sAML 46, XX, 15q- 12 52/ M RAEB-t high sAML complex 13 72/ M RAEB-t high sAML complex 14 75/ M RAEB int-I RAEB-I 46, XY 15 72/ M sAML/MDS - sAML complex 16 76/ M sAML/MDS - sAML 46, XY Dendrogram derived from hierarchical cluster analysis using the 1-correlation distance metrics and an average linkage algorithm. Dendrogram derived from hierarchical cluster analysis using the 1-correlation distance metrics and an average linkage algorithm.


2017 ◽  
Vol 7 (2) ◽  
pp. 76
Author(s):  
Ferry Kondo Lembang ◽  
Patresya Yulita Lessil ◽  
Salmon Notje Aulele

Regional gross domestic product is one of the important indicators to determine economic conditions in an area. Therefore it is very interesting to discuss and to determine the economic progress of a region. Cluster anlysis aims to classify objects based on the characteristics into cluster that have the properties that are relatively similar and clearly distinguish between one cluster agains another. The main objective of the research that classifies 33 provinces based on the value of regional gross domestic product at constant price in 2013 using hierarchical cluster analysis for average linkage method. The results showed that the cluster were carried out on 33 provinces in Indonesia formed 3 cluster with details of that cluster 1 consisting of Sumatera, Kalimantan, Sulawesi, Nusa Tenggara, Bali, Papua, Maluku and Jawa Tengah, DI Yogyakarta, and Banten, cluster 2 consisting of 1 provinces of DKI Jakarta and cluster 3 which consists of 2 provinces namely Jawa Barat dan Jawa Timur.


CAUCHY ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Cindy Cahyaning Astuti ◽  
Rahmania Sri Untari

This research was conducted in Sidoarjo District where source of data used from secondary data contained in the book <em>"Kabupaten Sidoarjo Dalam Angka 2016"</em> .In this research the authors chose 12 variables that can represent sub-district characteristics in Sidoarjo. The variable that represents the characteristics of the sub-district consists of four sectors namely geography, education, agriculture and industry. To determine the equitable geographical conditions, education, agriculture and industry each district, it would require an analysis to classify sub-districts based on the sub-district characteristics. Hierarchical cluster analysis is the analytical techniques used to classify or categorize the object of each case into a relatively homogeneous group expressed as a cluster. The results are expected to provide information about dominant sub-district characteristics and non-dominant sub-district characteristics in four sectors based on the results of the cluster is formed.


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