Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Nwadiugwu, Martin C

doi:10.1177/1177932220909851

Cited by 17 publications

(4 citation statements)

References 30 publications

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“…While BIRCH can solve big data sets with faster execution times, the quality of the generated clusters remains subpar [10]. The benefit of BIRCH is that it can infer the best subcluster that can be obtained while limiting input/output while gradually producing the highest quality clusters, which is useful when other methods struggle to handle outliers and huge datasets [19]. The steps of the BIRCH algorithm are as follows [20].…”

Section: Birchmentioning

confidence: 99%

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Rizalde,

Mubarak,

Ramadhan

et al. 2024

PREDATECS

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The hallmarks of Obsessive-Compulsive Disorder (OCD) are intrusive, anxiety-inducing thoughts (called obsessions) and associated repeated activities (called compulsions). To understand the patterns and relationships between OCD data that have been obtained, data will be grouped (clustering). In clustering using several clustering algorithms, namely K-Means, BIRCH, In this work, hierarchical clustering was used to identify the optimal cluster value comparison, and the Davies Bouldin Index (DBI) was used to confirm the results. Then the results of the best cluster value in processing OCD data are using the BIRCH algorithm in the K10 experiment which gets a value of 1.3. While the K-Means algorithm obtained the best cluster at K10 with a value obtained of 1.36 and the Hierarchical clustering algorithm also at the K10 value of 2.03. Thus in this study, the comparison results of the application of 3 clustering algorithms obtained results, namely the BIRCH algorithm shows the value of the resulting cluster is the best in clustering OCD data. This means that the BIRCH algorithm can be used to cluster OCD data more accurately and efficiently.

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Section: Birchmentioning

confidence: 99%

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Rizalde,

Mubarak,

Ramadhan

et al. 2024

PREDATECS

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show abstract

“…42 At that time, the study encountered and reported problems relating to gene prediction, annotation and alignment. Although bioinformatics methods and databases have since advanced 43 leading to improvements in multiple sequence alignment, determination of gene regulatory networks and prediction of subcellular localisation, many genes are still poorly annotated which represents a challenge for pathogen detection when implementing comparative genomics. However, these examples show how genomics can and have been proactively applied in the identification of virulent bio-threats.…”

Section: How Genomics Can Be Applied To Proactively Prevent An Outbreak?mentioning

confidence: 99%

Applied genomics for identification of virulent biothreats and for disease outbreak surveillance

Nwadiugwu

Monteiro

2022

Postgraduate Medical Journal

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Fortifying our preparedness to cope with biological threats by identifying and targeting virulence factors may be a preventative strategy for curtailing infectious disease outbreak. Virulence factors evoke successful pathogenic invasion, and the science and technology of genomics offers a way of identifying them, their agents and evolutionary ancestor. Genomics offers the possibility of deciphering if the release of a pathogen was intentional or natural by observing sequence and annotated data of the causative agent, and evidence of genetic engineering such as cloned vectors at restriction sites. However, to leverage and maximise the application of genomics to strengthen global interception system for real-time biothreat diagnostics, a complete genomic library of pathogenic and non-pathogenic agents will create a robust reference assembly that can be used to screen, characterise, track and trace new and existing strains. Encouraging ethical research sequencing pathogens found in animals and the environment, as well as creating a global space for collaboration will lead to effective global regulation and biosurveillance.

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“…In this method, similar data are grouped in the same cluster as in Figure 4, and the data outside these groups are determined as noise and either deleted or its value is changed to the closest cluster. K-means [27], DBSCAN [28], BIRCH [29] and OPTICS [30] are commonly used clustering algorithms. Schelling and Plant [31] made improvements to the standard Kmeans algorithm, which uses clustering method for noise detection, and increased its performance.…”

Section: Ensemble Filter (Ef)mentioning

confidence: 99%

A comprehensive review on data preprocessing techniques in data analysis

Çetin¹,

Yıldız²

2022

Pamukkale J Eng Sci

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Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Cited by 17 publications

References 30 publications

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Comparison of K-Means, BIRCH and Hierarchical Clustering Algorithms in Clustering OCD Symptom Data

Applied genomics for identification of virulent biothreats and for disease outbreak surveillance

A comprehensive review on data preprocessing techniques in data analysis

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