Comparison of hierarchical clustering methods (case study: data on poverty influence in North Sulawesi)

Mongi, Charles; Langi, Yohanes; Montolalu, Chriestie; Nainggolan, Nana Triapnita

doi:10.1088/1757-899x/567/1/012048

Cited by 8 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was demonstrated that cluster genesis through combined HC's linkage and dissimilarity algorithms & NNC is more reliable than individual optical assessment of NNC, where varying a map size in SOM will alter the association of inputs' weights to neurons, providing a new structure of the clusters. Moreover, HC-classification of predictors Na (9), SO 4 (11); pH (2), Turbidity (3),…”

Section: Discussionmentioning

confidence: 99%

“…6, is presented in Table 4, second row and third row display the number of links at different levels and their inconsistency coefficients, respectively. Here, inconsistency coefficient '0' corresponds to the links joining the pairs NO 3 (12) & F (15), Mg (6) & Cl (8), Ca (5) & As (16) and Na ( 9) & SO 4 (11) in Fig. 6, as there is no link joining the variables below them.…”

Section: Hierarchical Clustering (Hc)mentioning

confidence: 99%

“…To showcase this division, a stem diagram, Fig. 7, placing variables Na (9), SO 4 (11); pH (2), Turbidity (3), Mg (6), Cl (8), K (10), NO 3 (12), Fe (14), F (15), Total Coliforms ( 17), E. Coli (18); EC (1); TDS (13); HCO 3 (4); Hard (7); Ca (5), As ( 16) into clusters 7, 6, 5, 4, 3, 2, 1, respectively, is presented.…”

Section: Hierarchical Clustering (Hc)mentioning

confidence: 99%

“…A sound clustering algorithm efficiently undertakes the tasks of scaling a non-uniform data, analyzing a categorical, numerical and binary data, interpreting the results comprehensively, revealing significant features of a system and handling a high dimensional data. The most commonly used clustering methods are Hierarchical Clustering and K-Means Clustering [5][6][7][8][9][10][11][12][13][14][15][16] . In Hierarchical Clustering, a hierarchy of clusters is built, where each cluster contains similar characteristics data points of a system.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

Shahid

2023

Sci Rep

View full text Add to dashboard Cite

A comparison of neural network clustering (NNC) and hierarchical clustering (HC) is conducted to assess computing dominance of two machine learning (ML) methods for classifying a populous data of large number of variables into clusters. An accurate clustering disposition is imperative to investigate assembly-influence of predictors on a system over a course of time. Moreover, categorically designated representation of variables can assist in scaling down a wide data without loss of essential system knowledge. For NNC, a self-organizing map (SOM)-training was used on a local aqua system to learn distribution and topology of variables in an input space. Ternary features of SOM; sample hits, neighbouring weight distances and weight planes were investigated to institute an optical inference of system’s structural attributes. For HC, constitutional partitioning of the data was executed through a coupled dissimilarity-linkage matrix operation. The validation of this approach was established through a higher value of cophenetic coefficient. Additionally, an HC-feature of stem-division was used to determine cluster boundaries. SOM visuals reported two locations’ samples for remarkable concentration analogy and presence of 4 extremely out of range concentration parameter from among 16 samples. NNC analysis also demonstrated that singular conduct of 18 independent components over a period of time can be comparably inquired through aggregate influence of 6 clusters containing these components. However, a precise number of 7 clusters was retrieved through HC analysis for segmentation of the system. Composing elements of each cluster were also distinctly provided. It is concluded that simultaneous categorization of system’s predictors (water components) and inputs (locations) through NNC and HC is valid to the precision probability of 0.8, as compared to data segmentation conducted with either of the methods exclusively. It is also established that cluster genesis through combined HC’s linkage and dissimilarity algorithms and NNC is more reliable than individual optical assessment of NNC, where varying a map size in SOM will alter the association of inputs’ weights to neurons, providing a new consolidation of clusters.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Hierarchical Clustering (Hc)mentioning

confidence: 99%

Section: Hierarchical Clustering (Hc)mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

Shahid

2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Grouping is based on similarities or distance (dissimilarity). The clusters are aggregated according to a decreasing degree of similarity (or increasing degree of dissimilarity) into one single tree-like cluster, called a dendrogram [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

Assessment of the Decomposition of Oxo- and Biodegradable Packaging Using FTIR Spectroscopy

Markowicz

Szymańska‐Pulikowska

2021

Materials

View full text Add to dashboard Cite

The strength and resistance of plastics at the end of their service life can hinder their degradation. The solution to this problem may be materials made of biodegradable and oxo-biodegradable plastics. The aim of this research was to determine the degree and nature of changes in the composition and structure of composted biodegradable and oxo-biodegradable bags. The research involved shopping bags and waste bags available on the Polish market. The composting of the samples was conducted in an industrial composting plant. As a result of the research, only some of the composted samples decomposed. After composting, all samples were analysed using FTIR (Fourier Transformation Infrared) spectroscopy. Carbonyl index and hierarchical cluster analysis method was used to detect similarities between the spectra of the new samples. The analysis of the obtained results showed that FTIR spectroscopy is a method that can be used to confirm the degradation and detect similarities in the structure of the analysed materials. The analysis of spectra obtained with the use of FTIR spectroscopy indicated the presence of compounds that may be a potential source of compost contamination. Plastics with certificates confirming their biodegradability and compostability should be completely biodegradable, i.e., each element used in their production should be biodegradable and safe for the environment.

show abstract

Clustering of Countries Based on the Associated Social Contact Patterns in Epidemiological Modelling

Korir

Vizi

2023

Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics

View full text Add to dashboard Cite

Comparison of hierarchical clustering methods (case study: data on poverty influence in North Sulawesi)

Cited by 8 publications

References 6 publications

Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

Comparison of hierarchical clustering and neural network clustering: an analysis on precision dominance

Assessment of the Decomposition of Oxo- and Biodegradable Packaging Using FTIR Spectroscopy

Clustering of Countries Based on the Associated Social Contact Patterns in Epidemiological Modelling

Contact Info

Product

Resources

About