Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time

Hassan, Syed Imtiyaz; Samad, Afreen; Ahmad, Omair; Alam, Afshar

doi:10.1007/s41870-019-00406-7

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…Since agglomerative clustering begins by assigning each datapoint a cluster, very few assumptions are made about the data. This is one of the strengths of agglomerative clustering, compared to other clustering methods, such as k-means (Hassan et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…It is particularly suited for datasets where clusters maybe unevenly shaped, of unequal size and unequally distributed across parameter space (Hirano et al, 2004 ). In this hierarchical cluster analysis, the model is initialized by assuming that each datapoint is an individual cluster (Feeny et al, 2020 ; Hassan et al, 2020 ). Similarity and linkage are the two parameters of greatest importance for agglomerative clustering.…”

Section: Methodsmentioning

confidence: 99%

“…To determine the optimal number of clusters, the Silhouette and Davies-Bouldin scores were used (Feeny et al, 2020 ; Hassan et al, 2020 ). The Silhouette score is used to determine the separation distance between the resulting clusters (i.e., measures how similar an object is to its own cluster, compared to other clusters).…”

Section: Methodsmentioning

confidence: 99%

“…Hierarchical agglomerative clustering, is a unsupervised, bottom-up, machine learning approach that can identify subgroups from complex data and provide an opportunity to classify clinical patterns as well as create novel representations of clinical profiles (Hassan et al, 2020 ). There are substantial implications for research on concussion subtypes, but also the utilization of machine learning to help in interpreting overall assessment results and summarizing multiple parameters to identify which features or combination of features discriminate between clinical profiles.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Subtyping Concussion Using a Clustering Approach

Rosenblatt

Harriss

Babul

et al. 2021

Front. Hum. Neurosci.

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Background: Concussion subtypes are typically organized into commonly affected symptom areas or a combination of affected systems, an approach that may be flawed by bias in conceptualization or the inherent limitations of interdisciplinary expertise.Objective: The purpose of this study was to determine whether a bottom-up, unsupervised, machine learning approach, could more accurately support concussion subtyping.Methods: Initial patient intake data as well as objective outcome measures including, the Patient-Reported Outcomes Measurement Information System (PROMIS), Dizziness Handicap Inventory (DHI), Pain Catastrophizing Scale (PCS), and Immediate Post-Concussion Assessment and Cognitive Testing Tool (ImPACT) were retrospectively extracted from the Advance Concussion Clinic's database. A correlation matrix and principal component analysis (PCA) were used to reduce the dimensionality of the dataset. Sklearn's agglomerative clustering algorithm was then applied, and the optimal number of clusters within the patient database were generated. Between-group comparisons among the formed clusters were performed using a Mann-Whitney U test.Results: Two hundred seventy-five patients within the clinics database were analyzed. Five distinct clusters emerged from the data when maximizing the Silhouette score (0.36) and minimizing the Davies-Bouldin score (0.83). Concussion subtypes derived demonstrated clinically distinct profiles, with statistically significant differences (p < 0.05) between all five clusters.Conclusion: This machine learning approach enabled the identification and characterization of five distinct concussion subtypes, which were best understood according to levels of complexity, ranging from Extremely Complex to Minimally Complex. Understanding concussion in terms of Complexity with the utilization of artificial intelligence, could provide a more accurate concussion classification or subtype approach; one that better reflects the true heterogeneity and complex system disruptions associated with mild traumatic brain injury.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning for Subtyping Concussion Using a Clustering Approach

Rosenblatt

Harriss

Babul

et al. 2021

Front. Hum. Neurosci.

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“…Apabila maka objek-i dapat masuk ke 2 cluster tertentu. Sedangkap apabila maka objek ke-i masuk dalam cluster yang tidak tepat [15]. Silhouette Index diperoleh dari rata-rata hasil untuk setiap objek ke-.…”

Section: Silhouette Indexunclassified

Recommendation System for Specialization Selection Using K-Means Density Canopy

Ananda¹,

Naf’an²,

Arifa³

et al. 2020

RESTI

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The carelessly selection of specialization course leaves some students with difficulty. Therefore, it is needed a recommendation system to solve it. Several approaches could be used to build the system, one of them was K-Means. K-Means required the number of initial centroid at random, so its result was not yet optimal. To determine the optimal initial centroid, Density Canopy (DC) algorithms had been proposed. In this research, DC and K-Means (DCKM) was implemented to build the recommendation system in the problem. The alpha criterion was also proposed to improve the performance of DCKM. The academic quality dataset in the 2018 informatics programs students of ITTP was used. There were three main stages in the system, namely determination of the weight of the course in dataset, implementation of DCKM, and determination of specialization recommendations. The results showed that the system by using DCKM has good quality based on the Silhouette results (at least 0.655). The system also used standar valuation scale in ITTP and silhouette index in the process of system. The results showed that 176 (65.91%) students were recommended in IT specialization, 25 (9.36%) students were recommended in MM specialization and 66 (24.7%) students were recommended in SC specialization.

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Digging for Significant Genes in Microarray Expression Data Based on Systematic Sampling and Hierarchal Clustering Algorithm

Mohammed

2021

GeNeDis 2020

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Partitioning and hierarchical based clustering: a comparative empirical assessment on internal and external indices, accuracy, and time

Cited by 13 publications

References 26 publications

Machine Learning for Subtyping Concussion Using a Clustering Approach

Machine Learning for Subtyping Concussion Using a Clustering Approach

Recommendation System for Specialization Selection Using K-Means Density Canopy

Digging for Significant Genes in Microarray Expression Data Based on Systematic Sampling and Hierarchal Clustering Algorithm

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