K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Ikotun, Abiodun M.; Almutari, Mubarak S.; Ezugwu, Absalom E.

doi:10.3390/app112311246

Cited by 50 publications

(28 citation statements)

References 189 publications

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“…e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54]. us, there is a demand for a practical ontological framework for managing documents and retrieving information based on the user query.…”

Section: Literature Reviewmentioning

confidence: 99%

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Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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In recent times, artificial intelligence (AI) methods have been applied in document and content management to make decisions and improve the organization’s functionalities. However, the lack of semantics and restricted metadata hinders the current document management technique from achieving a better outcome. E-Government activities demand a sophisticated approach to handle a large corpus of data and produce valuable insights. There is a lack of methods to manage and retrieve bilingual (Arabic and English) documents. Therefore, the study aims to develop an ontology-based AI framework for managing documents. A testbed is employed to simulate the existing and proposed framework for the performance evaluation. Initially, a data extraction methodology is utilized to extract Arabic and English content from 77 documents. Researchers developed a bilingual dictionary to teach the proposed information retrieval technique. A classifier based on the Naïve Bayes approach is designed to identify the documents’ relations. Finally, a ranking approach based on link analysis is used for ranking the documents according to the users’ queries. The benchmark evaluation metrics are applied to measure the performance of the proposed ontological framework. The findings suggest that the proposed framework offers supreme results and outperforms the existing framework.

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Section: Literature Reviewmentioning

confidence: 99%

“…erefore, developing an ontological framework for document management can support organizations in satisfying their stakeholders. In addition, the role of natural language processing (NLP) in the ontological framework enables individuals to interact with the system in their natural language [52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Alothman

Sait

2022

Computational Intelligence and Neuroscience

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“…It iteratively searches for a global optimum solution using a population of candidate solutions through optimization of a given objective function. SOS has been reported to have a better searching quality and searching efficiency when compared with other metaheuristic algorithms such as GWO, which was noted as being superior to other existing metaheuristic algorithms in several reported comparisons [ 50 , 51 ]. The SOS algorithm is credited with performance stability because it does not use tunning parameters.…”

Section: Introductionmentioning

confidence: 99%

“…This paper proposes a new hybrid clustering algorithm called SOSK-means, which combines the SOS algorithm with the classical K-means algorithm for automatic clustering. The traditional K-means clustering algorithm has been hybridized with some of the existing nature-inspired metaheuristic algorithms [ 51 ]. However, the focus in many of these hybridized algorithms is on enhancing the capability of the respective nature-inspired algorithm in handling automatic clustering problems.…”

Section: Introductionmentioning

confidence: 99%

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Ikotun

Ezugwu

2022

PLoS ONE

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Kmeans clustering algorithm is an iterative unsupervised learning algorithm that tries to partition the given dataset into k pre-defined distinct non-overlapping clusters where each data point belongs to only one group. However, its performance is affected by its sensitivity to the initial cluster centroids with the possibility of convergence into local optimum and specification of cluster number as the input parameter. Recently, the hybridization of metaheuristics algorithms with the K-Means algorithm has been explored to address these problems and effectively improve the algorithm’s performance. Nonetheless, most metaheuristics algorithms require rigorous parameter tunning to achieve an optimum result. This paper proposes a hybrid clustering method that combines the well-known symbiotic organisms search algorithm with K-Means using the SOS as a global search metaheuristic for generating the optimum initial cluster centroids for the K-Means. The SOS algorithm is more of a parameter-free metaheuristic with excellent search quality that only requires initialising a single control parameter. The performance of the proposed algorithm is investigated by comparing it with the classical SOS, classical K-means and other existing hybrids clustering algorithms on eleven (11) UCI Machine Learning Repository datasets and one artificial dataset. The results from the extensive computational experimentation show improved performance of the hybrid SOSK-Means for solving automatic clustering compared to the standard K-Means, symbiotic organisms search clustering methods and other hybrid clustering approaches.

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“…As a result, data with a low level of similarity will occupy a hierarchy that is far apart [2] [3]. Clustering with a partition-based clustering approach is carried out by grouping the data and sorting the analyzed data into existing clusters [4] [5].…”

Section: Introductionmentioning

confidence: 99%

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

Rachmatullah

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Clustering is a technique in data mining that groups a set of data into groups (clusters) of similar data. In general, there are two methods of clustering, namely the hierarchical method and the partition method. One of the most commonly used partition clustering methods in clustering is K-Means. The use of K-means method has been widely used in various fields with various purposes. Many research has been carried out to improve the performance of the K-Means method, for example, by modifying the method of determining the initial centroid or determining the appropriate number of clusters. In this research, the modification of the K-Means algorithm was carried out in calculating the distance by considering the correlation value between attributes. Attributes that have a high correlation value are assumed to have similar characteristics so that they determine the location of data in a particular cluster. The steps of the proposed method are: calculating the correlation value between attributes, determining the cluster centroid, calculating the distance by considering the value of correlation, and determining the data into certain clusters. The first contribution of this research is to propose a new distance calculation technique in the K-Means algorithm by considering correlation and the second contribution is to apply the proposed algorithm to a specific dataset, namely Iris dataset. In this research, the performance calculation of the modified algorithm was also carried out. From the experimental results using the Iris dataset, the proposed modification of the K-Means algorithm has fewer iterations than the original K-Means method, so that it requires less processing time. The original K-Means method requires 8 iterations, while the proposed method requires only 6 iterations. The proposed method also produces a higher accuracy rate of 89.33% than the original K-Means method, which is 82.67%.

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K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Cited by 50 publications

References 189 publications

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

Boosting k-means clustering with symbiotic organisms search for automatic clustering problems

Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation

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