Efficient algorithms for agglomerative hierarchical clustering methods

1984 ◽  
Vol 1 (1) ◽  
pp. 7-24 ◽  
Author(s):  
William H. E. Day ◽  
Herbert Edelsbrunner
Keyword(s):  
Hierarchical Clustering ◽  
Efficient Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Methods ◽  
Hierarchical Clustering Methods

scholarly journals Penerapan Metode Angglomerative Hierarchical Clustering untuk Klasifikasi Kabupaten/Kota di Propinsi Jawa Timur Berdasarkan Kualitas Pelayanan Keluarga Berencana

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 25
Author(s):  
Alfi Fadliana ◽  
Fachrur Rozi
Keyword(s):  
Hierarchical Clustering ◽  
Good Condition ◽  
Cluster Solution ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Methods ◽  
Cophenetic Correlation ◽  
Service Personnel ◽  
Medium Condition ◽  
Average Linkage ◽  
Hierarchical Clustering Methods

Agglomerative hierarchical clustering methods is cluster analysis method whose primary purpose is to group objects based on its characteristics, it begins with the individual objects until the objects are fused into a single cluster. Agglomerative hierarchical clustering methods are divided into single linkage, complete linkage, average linkage, and ward. This research compared the four agglomerative hierarchical clustering methods in order to get the best cluster solution in the case of the classification of regencies/cities in East Java province based on the quality of “Keluarga Berencana” (KB) services. The results of this research showed that based on calculation of cophenetic correlation coefficient, the best cluster solution is produced by average linkage method. This method obtained four clusters with the different characteristics. Cluster 1 has an “extremely bad condition” on the qualification of KB clinics and the competence of KB service personnel. Cluster 2 has a “good condition” on the qualification of KB clinics and “bad condition” on the competence of KB service personnel. Cluster 3 has a “bad condition” on the qualification of KB clinics and “medium condition” on the competence of KB service personnel. Cluster 4 have a “medium condition” on the qualification of KB clinics and a “good condition” on the competence of KB service personnel

scholarly journals Machine-learning identification of asteroid groups

2019 ◽  
Vol 488 (1) ◽  
pp. 1377-1386 ◽  
Author(s):  
V Carruba ◽  
S Aljbaae ◽  
A Lucchini
Keyword(s):  
Machine Learning ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Family Members ◽  
Parent Body ◽  
Clustering Methods ◽  
New Family ◽  
Hierarchical Clustering Methods ◽  
Rotational Failure ◽  
Asteroid Families

ABSTRACT Asteroid families are groups of asteroids that share a common origin. They can be the outcome of a collision or be the result of the rotational failure of a parent body or its satellites. Collisional asteroid families have been identified for several decades using hierarchical clustering methods (HCMs) in proper elements domains. In this method, the distance of an asteroid from a reference body is computed, and, if it is less than a critical value, the asteroid is added to the family list. The process is then repeated with the new object as a reference, until no new family members are found. Recently, new machine-learning clustering algorithms have been introduced for the purpose of cluster classification. Here, we apply supervised-learning hierarchical clustering algorithms for the purpose of asteroid families identification. The accuracy, precision, and recall values of results obtained with the new method, when compared with classical HCM, show that this approach is able to found family members with an accuracy above 89.5 per cent, and that all asteroid previously identified as family members by traditional methods are consistently retrieved. Values of the areas under the curve coefficients below Receiver Operating Characteristic curves are also optimal, with values consistently above 85 per cent. Overall, we identify 6 new families and 13 new clumps in regions where the method can be applied that appear to be consistent and homogeneous in terms of physical and taxonomic properties. Machine-learning clustering algorithms can, therefore, be very efficient and fast tools for the problem of asteroid family identification.

scholarly journals Using hierarchical clustering to explore patterns of deprivation among English local authorities

2019 ◽  
Vol 42 (4) ◽  
pp. 772-777
Author(s):  
Steven L Senior
Keyword(s):  
Hierarchical Clustering ◽  
Demographic Characteristics ◽  
Homogeneous Group ◽  
Statistical Testing ◽  
Clustering Methods ◽  
Local Authorities ◽  
Multiple Deprivation ◽  
Hierarchical Clustering Methods ◽  
Different Levels ◽  
Indices Of Multiple Deprivation

Abstract Background The English Indices of Multiple Deprivation (IMD) is widely used as a measure of deprivation. However, similarly ranked areas can differ substantially in the underlying domains of deprivation. These domains contain a richer set of data that might be useful for classifying local authorities. Clustering methods offer a set of techniques to identify groups of areas with similar patterns of deprivation. Methods Hierarchical agglomerative (i.e. bottom-up) clustering methods were applied to domain scores for 152 upper tier local authorities. Advances in statistical testing allow clusters to be identified that are unlikely to have arisen from random partitioning of a homogeneous group. The resulting clusters are described in terms of their subdomain scores and basic geographic and demographic characteristics. Results Five statistically significant clusters of local authorities were identified. These clusters only partially reflect different levels of overall deprivation. In particular, two clusters share similar overall IMD scores but have contrasting patterns of deprivation. Conclusion Hierarchical clustering methods identify five distinct clusters that do not correspond closely to quintiles of deprivation. This approach may help to distinguish between places that face similar underlying challenges, and places that appear similar in terms of overall deprivation scores, but that face different challenges.

Research on NMF Based Hierarchical Clustering Methods

2010 ◽  
Vol 439-440 ◽  
pp. 1306-1311
Author(s):  
Fang Li ◽  
Qun Xiong Zhu
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Clustering Method ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Methods

LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

scholarly journals Evaluation of the Gower coefficient modifications in hierarchical clustering

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Zdeněk Šulc ◽  
Martin Matějka ◽  
Jiří Procházka ◽  
Hana Řezanková
Keyword(s):  
Hierarchical Clustering ◽  
Mixed Type ◽  
Similarity Measures ◽  
Rand Index ◽  
Clustering Methods ◽  
Single Linkage ◽  
Linkage Methods ◽  
Hierarchical Clustering Methods ◽  
Nominal Variables

This paper thoroughly examines three recently introduced modifications of the Gower coefficient, which were determined for data with mixed-type variables in hierarchical clustering. On the contrary to the original Gower coefficient, which only recognizes if two categories match or not in the case of nominal variables, the examined modifications offer three different approaches to measuring the similarity between categories. The examined dissimilarity measures are compared and evaluated regarding the quality of their clusters measured by three internal indices (Dunn, silhouette, McClain) and regarding their classification abilities measured by the Rand index. The comparison is performed on 810 generated datasets. In the analysis, the performance of the similarity measures is evaluated by different data characteristics (the number of variables, the number of categories, the distance of clusters, etc.) and by different hierarchical clustering methods (average, complete, McQuitty and single linkage methods). As a result, two modifications are recommended for the use in practice.

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