Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

Ezugwu, Absalom E.; Shukla, Amit K.; Agbaje, Moyinoluwa B.; Oyelade, Olaide N.; José-García, Adán; Agushaka, Jeffrey O.

doi:10.1007/s00521-020-05395-4

Cited by 110 publications

(52 citation statements)

References 195 publications

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“…Mut-over can also be used for mutation-crossover as a recombination strategy. Third, mut-overing with F and HI eliminates the issue of global and local optima that can occur with other algorithms such as K-means [58]. Thanks to this recombined evolutionary operator [57], our proposed algorithm is even more stable and robust.…”

Section: Our Proposed Frameworkmentioning

confidence: 93%

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Hassan

Rashid

Mirjalili

2021

Complex Intell. Syst.

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It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

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Section: Our Proposed Frameworkmentioning

confidence: 93%

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Hassan

Rashid

Mirjalili

2021

Complex Intell. Syst.

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“…VOSviewer software is a tool for constructing and visualizing bibliometric maps of items, such as journals, research, or individual publications. These maps can be created based on citation, bibliographic coupling, co-citation, or co-authorship relations (Ezugwu et al, 2020). The bibliometric analysis software also offers text mining functionality that can be used to construct and visualize co-occurrence networks of important terms extracted from a body of scientific literature (see www.vosviewer.com).…”

Section: Bibliometric Protocolmentioning

confidence: 99%

“…In the visualization, items within the same cluster are marked with the same color, and colors indicate the cluster to which a journal was assigned by the clustering technique implemented by the VOSviewer software. The circular node may represent the items, and their sizes may vary depending on the weight of the article (Ezugwu et al, 2020).…”

Section: Bibliometric Analysismentioning

confidence: 99%

Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

Chiroma

Ezugwu

Jauro

et al. 2020

Preprint

Self Cite

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Background and Objective: The COVID-19 pandemic has caused severe mortality across the globe with the USA as the current epicenter, although the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective. To the best of the authors knowledge, no comprehensive survey with bibliometric analysis has been conducted on the adoption of machine learning for fighting COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine-learning-based technologies to fight the COVID-19 pandemic from a different perspective, including an extensive systematic literature review and a bibliometric analysis. Methods: A literature survey methodology is applied to retrieve data from academic databases, and a bibliometric technique is subsequently employed to analyze the accessed records. Moreover, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis, and analysis are presented. The convolutional neural network (CNN) is found mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest computed tomography (CT) scan images. Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed. Finally, a new perspective is proposed to solve the challenges identified as directions for future research. We believe that the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators. Results: The findings in this study reveal that machine-learning-based COVID-19 diagnostic tools received the most considerable attention from researchers. Specifically, the analyses of the results show that energy and resources are more dispensed toward COVID-19 automated diagnostic tools, while COVID-19 drugs and vaccine development remain grossly underexploited. Moreover, the machine-learning-based algorithm predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images. Conclusions: The challenges hindering practical work on the application of machine-learning-based technologies to fight COVID-19 and a new perspective to solve the identified problems are presented in this study. We believe that the presented survey with bibliometric analysis can help researchers determine areas that need further development and identify potential collaborators at author, country, and institutional levels to advance research in the focused area of machine learning application for disease control.

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“…Overlapping clustering algorithms are unsupervised methods for knowledge extraction from data. These algorithms classify a set of objects so that each object can belong to one or more groups [1]. Overlapping clustering algorithms are momentous because a large number of real-world datasets (such as textual data, computational biology, and social networks) contain innate overlapping.…”

Section: Introductionmentioning

confidence: 99%

FOCT: Fast Overlapping Clustering for Textual Data

Khazaei¹,

Khaleghzadeh

Ghasemzadeh³

2021

IEEE Access

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Automatic clustering algorithms: a systematic review and bibliometric analysis of relevant literature

Cited by 110 publications

References 195 publications

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

FOCT: Fast Overlapping Clustering for Textual Data

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