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

2020 ◽  
Vol 17 (24) ◽  
pp. 9515
Author(s):  
Jianwei Bu ◽  
Wei Liu ◽  
Zhao Pan ◽  
Kang Ling
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Minimum Variance ◽  
Single Linkage ◽  
Complete Linkage ◽  
Advantages And Disadvantages ◽  
Average Linkage ◽  
Analysis Methods ◽  
Hydrochemical Classification

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.

scholarly journals Perbandingan Metode Single Linkage, Complete Linkage Dan Average Linkage dalam Pengelompokan Kecamatan Berdasarkan Variabel Jenis Ternak Kabupaten Sidoarjo

Jurnal INFORM ◽  
2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Sulthan Fikri Mu'afa ◽  
Nurissaidah Ulinnuha
Keyword(s):  
Farm Animals ◽  
Single Linkage ◽  
Daily Lives ◽  
Food Ingredients ◽  
Complete Linkage ◽  
Material Sources ◽  
Average Linkage ◽  
Linkage Method ◽  
Livestock Products ◽  
Labor Resources

Livestock products are widely used by the community in their daily lives, for example as food ingredients, industrial material sources, labor resources, fertilizer sources and energy sources. This study aims to cluster livestock potential with data on livestock population in Sidoarjo Regency in 2017 with single linkage, complete linkage and average linkage method and comparing performance of the methods. In this cluster, the data will be grouped into 3 clusters. The results of the three clusters were obtained by sixteen sub-districts in the first cluster with the potential for low livestock and each one in the second and third clusters for single linkage and average linkage. While complete linkage obtained fifteen sub-districts in the first cluster with high potential for livestock, two sub-districts in the second cluster with the potential of medium livestock and one sub-district in the third cluster with the potential for high farm animals. In the comparison of the standard deviation ratio value, the smallest value of 0.222 is obtained by complete linkage, which shows that complete linkage is better than single linkage and average linkage in the case of subgrouping based on Sidoarjo regency livestock types.

Identifying Rogue Air Temperature Stations Using Cluster Analysis of Percentile Trends

2005 ◽  
Vol 18 (8) ◽  
pp. 1275-1287 ◽  
Author(s):  
Scott M. Robeson ◽  
Jeffrey A. Doty
Keyword(s):  
Cluster Analysis ◽  
Air Temperature ◽  
Large Body ◽  
Large Temperature ◽  
Daily Maximum ◽  
Climatic Data ◽  
Single Linkage ◽  
Frequency Distributions ◽  
Temperature Trends ◽  
Average Linkage

Abstract A new and efficient method for identifying “rogue” air temperature stations—locations with unusually large air temperature trends—is presented. Instrumentation problems and spatially unrepresentative local climates are sometimes more apparent in air temperature extremes, yet can have more subtle impacts on variations in mean air temperature. As a result, using data from over 1300 stations in North America, the tails of daily air temperature frequency distributions were examined for unusual trends. In particular, linear trends in the 5th percentile of daily minimum air temperature during the winter months and the 95th percentile of daily maximum air temperature during the summer were analyzed. Cluster analysis then was used to identify stations that were distinct from other locations. Both single- and average linkage clustering were evaluated. By identifying individual stations along the entire periphery of the percentile trend space, single-linkage clustering appears to produce better results than that of average linkage. Average linkage clustering tends to group together several stations with large trends; however, only a handful of these stations appear distinctly different from the large body of trends toward the center of the percentile trend space. Maps of the rogue stations show that most are in close proximity to numerous other stations that were not grouped into the rogue cluster, making it unlikely that the unusually large temperature trends were due to regional climatic variations. As with all approaches for evaluating data quality, time series plots and station history information also must be inspected to more fully understand inhomogeneous variations in historical climatic data.

Cluster analysis of late Holocene pollen trends in Ontario

1991 ◽  
Vol 69 (8) ◽  
pp. 1719-1730 ◽  
Author(s):  
Ian D. Campbell ◽  
J. H. McAndrews
Keyword(s):  
Cluster Analysis ◽  
Geometric Mean ◽  
Ice Age ◽  
White Pine ◽  
Single Linkage ◽  
Complete Linkage ◽  
Similarity Indices ◽  
Species Abundances ◽  
Climatic Cooling ◽  
Past 1000 Years

Cluster analysis of Ontario pollen stratigraphies demonstrates similar regional successions during the past 1000 years. Seven character states qualitatively describe the behaviour of the pollen percentage trends for each taxon: 0, absent; 1, present with no visible trend but high noise; 2, rising through time; 3, falling through time; 4, rise–fall; 5, fall–rise; and 6, stable through time. The three similarity indices (S) used were of the form S equals the number of characters in agreement divided by the number of informative characters. The three clustering techniques used are single linkage, complete linkage, and unpaired weighted geometric mean analysis. Single linkage and unpaired weighted geometric mean analysis showed a north–south division with all three indices; complete linkage showed only rare local groupings with all three indices. The division between the two clusters falls just south of Lake Nipissing. All successions indicate climatic cooling; the clusters reflect southward movement of the centres of species abundances, particularly white pine. The method identifies regions of similar vegetation dynamics. Key words: cluster analysis, forest dynamics, Holocene, Little Ice Age, Ontario, palynology.

scholarly journals PENCARIAN CLUSTER OPTIMUM PADA SINGLE LINKAGE, COMPLETE LINKAGE DAN AVERAGE LINKAGE

2019 ◽  
Vol 8 (3) ◽  
Author(s):  
Nur Asiska, Neva Satyahadewi, Hendra Perdana
Keyword(s):  
Global Optimum ◽  
Single Linkage ◽  
Complete Linkage ◽  
Average Linkage

Analisis cluster merupakan teknik multivariat yang digunakan untuk mengelompokkan objek/kasus (responden) menjadi kelompok-kelompok yang lebih kecil dimana setiap kelompok berisi objek/kasus yang mirip satu sama lain. Dalam analisis cluster dua prosedur yang digunakan untuk pengelompokan yaitu analisis cluster hierarki dan non-hierarki. Penentuan jumlah cluster optimum yang tepat untuk digunakan diperoleh melalui identifikasi pola pergerakan varian pada cluster yang mencapai global optimum. Penemuan posisi cluster yang mencapai global optimum pada pola pergerakan varian diperoleh melalui penerapan metode valley-tracing. Pada penelitian, digunakan penerapan analisis cluster hierarki untuk mengelompokkan kabupaten/kota di Kalimantan Barat berdasarkan indikator IPM. Dari hasil analisis pembentukan cluster optimum pada metode single linkage diperoleh cluster optimum sebanyak 4 cluster. Pada metode complete linkage diperoleh cluster optimum sebanyak 5 cluster. Metode average linkage menghasilkan cluster optimum sebanyak 5 cluster Kata Kunci : Analisis Multivariat, Analisis Cluster, Cluster Optimum 

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 Analisa Hasil Pengelompokan Wilayah Kejadian Non-Kebakaran Menggunakan Agglomerative Hierachical Clustering di Semarang

2021 ◽  
Vol 15 (2) ◽  
pp. 63
Author(s):  
Desy Exasanti ◽  
Arief Jananto
Keyword(s):  
Hierarchical Clustering ◽  
Manhattan Distance ◽  
Agglomerative Hierarchical Clustering ◽  
Single Linkage ◽  
Bottom Up ◽  
Environment Analysis ◽  
Complete Linkage ◽  
Average Linkage

Abstrak−Klasterisasi merupakan metode pengelompokan dari data yang sudah diketahui label kelasnya untuk menemukan klaster baru dari hasil observasi. Dalam klasterisasi banyak metode yaitu metode terpusat, hirarki, kepadatan dan berbasis kisi, namun dalam penelitian yang dilakukan ini dipilih metode berbasis hirarki. Metode hirarki ini bekerja melakukan pengelompokan objek dengan membentuk hirarki klaster namun bukan berarti selalu digambarkan dengan hirarki dalam organsasi. Dipilihnya Agglomerative Hierarchical Clustering dimana merupakan jenis dari bawah ke atas atau biasa disebut (bottom-up) dalam metode ini objek yang akan diuji dianggap sebagai objek tunggal sebagai klaster dan lalu dilakukan iterasi untuk menemukan klaster-klaster yang lebih besar. Data yang akan digunakan adalah data non-kebakaran pada Dinas Pemadam Kebakaran Kota Semarang ynng mana akan dilakukan pengelompokan wilayah penanganan non-kebakaran. Dinas Pemadam Kebakaran melakukan penanganan bukan hanya kebakaran saja namun ada banyak hal yang sebenarnya dapat ditangani oleh petugas pemadam kebakaran, kejadian non-kebakaran ada beberapa seperti evakuasi reptil, evakuasi kucing, penyelamatan korban kecelakaan dan lain sebagainya. Dari data non-kebakaran dari 16 kecamatan di Kota Semarang pada tahun 2019 akan dilakukan uji menggunakan tiga algoritma yaitu Single Lingkage, Average Linkage dan Complete Linkage . Adapun dari algoritma Single Linkage dilakukan prosedur pemusatan dari jarak terkecil antar objek data, algoritma Average Linkage dilakukan prosedur dari jarak rata-rata objek data, sedangkan jika algoritma Complete Linkage dilakukan prosedur pemusatan dari jarak yang terbesar. Implementasi dan visualiasi dari data uji coba yang dilakukan di penilitian ini menggunakan tools WEKA 3.8.4, Wakaito Environment Analysis for Knowledge atau yang biasa dikenal dengan WEKA ini merupakan software yang menggunakan bahasa pemrograman java. Dari dataset 380 data diambil sampel 100 data untuk diuji mengunakan WEKA menggunakan metode perhtungan jarak Manhattan Distance dengan 3 cluster. Hasil dari data uji coba dapat divisualisasikan dengan visualisasi dendogram pada fitur visualize tree  dan jika dilakukan visualisasi dalam bentuk grafik dapat dilakukan menggunakan fitur visualize clusters assignment.

scholarly journals Klasszikus klaszterező algoritmusok módosítása körút alapon

2021 ◽  
Vol 11 (4) ◽  
pp. 81-86
Author(s):  
Anita Agárdi
Keyword(s):  
Single Linkage ◽  
Complete Linkage ◽  
Average Linkage

Jelen cikkben a klasszikus klaszterező algoritmusok egy módosítását mutatom be. A cikkben egy olyan módszert mutatok be, amellyel a klaszterező algoritmusok maguk határozzák meg a klaszterhatárokat, azt, hogy hány csoportra bontsák az adatsor elemeit. A klaszterezés egy olyan adatbányászati módszer, ahol az egymással hasonló elemek azonos klaszterbe, míg az egymástól különböző elemek külön klaszterbe kerülnek. Jelen cikkben egy partíciós algoritmust (K-Means) és a hierarchikus módszereket (Single Linkage, Complete Linkage, Average Linkage, Ward, Centroid) mutatom be. A futási eredmények azt mutatják, hogy a klaszterezési algoritmusoknak többé-kevésbé sikerült kialakítaniuk a klasztereket anélkül, hogy bemenetként a klaszterszámot várnánk.

scholarly journals Clustering Provinces in Indonesia based on Community Welfare Indicators

2020 ◽  
Vol 20 (1) ◽  
pp. 56-63
Author(s):  
Sekti Kartika Dini ◽  
Achmad Fauzan
Keyword(s):  
Cluster Analysis ◽  
Data Analysis ◽  
Policy Formulation ◽  
Main Task ◽  
Welfare Indicators ◽  
The Social ◽  
The Republic ◽  
The Government ◽  
Different Characteristics ◽  
Linkage Cluster

The Preamble of the 1945 Constitution of the Republic of Indonesia explicitly states that the main task of the government of the Republic of Indonesia is to advance general prosperity, to develop the nation's intellectual life, and to realize social justice for all Indonesian people. Social inequality is a problem that is still faced by Indonesian people today. To solve the problem required supporting data analysis as a basis for policy formulation. This research was conducted with the aim of clustering provinces in Indonesia based on community welfare indicators using K-Means cluster analysis. K-Means cluster analysis is chosen based on the variance value (0.101), which is smaller than the variance value in the average linkage cluster analysis (0.152). Based on data analysis, provinces in Indonesia are clustered into three where the first cluster consists of 21 provinces, the second cluster consists of 3 provinces, and the third cluster consists of 10 provinces. Each cluster has different characteristics that can be of concern to the parties concerned to overcome the social welfare gap. Besides, in order cluster results are more easily understood, visualization of results is added with a Geographic Information System (GIS) using Indonesian maps accompanied by differences in color gradations for each cluster

scholarly journals Validity studies among hierarchical methods of cluster analysis using cophenetic correlation coefficient

2019 ◽  
Vol 7 (2A) ◽  
Author(s):  
Priscilla Ramos Carvalho ◽  
Casimiro Sepúlveda Munita ◽  
André Luiz Lapolli
Keyword(s):  
Cluster Analysis ◽  
Correlation Coefficient ◽  
Single Linkage ◽  
Cophenetic Correlation ◽  
Data Set ◽  
Archaeological Data ◽  
Average Linkage ◽  
Linkage Method ◽  
Cophenetic Correlation Coefficient ◽  
Statistical Program

The literature presents many methods for partitioning of data set, and is difficult choose which is the most suitable, since the various combinations of methods based on different measures of dissimilarity can lead to different patterns of grouping and false interpretations. Nevertheless, little effort has been expended in evaluating these methods empirically using an archaeological data set. In this way, the objective of this work is make a comparative study of the different cluster analysis methods and identify which is the most appropriate. For this, the study was carried out using a data set of 45 samples of ceramic fragments, analyzed by instrumental neutron activation analysis (INAA). The methods used for this study were: Single linkage, Complete linkage, Average linkage, Centroid and Ward. The validation was done using the cophenetic correlation coefficient and comparing these values the average linkage method obtained better results. A script of the statistical program R with some functions was created to obtain the cophenetic correlation. By means of these values was possible to choose the most appropriate method to be used in the data set.

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