Introduction to Evolutionary Data Clustering and Its Applications

Aljarah, Ibrahim; Habib, Maria; Faris, Hossam; Mirjalili, Seyedali

doi:10.1007/978-981-33-4191-3_1

Cited by 3 publications

(4 citation statements)

References 69 publications

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“…Steps 2 and 3 are repeated until no data changes its cluster membership, or the criterion function does not improve during a number of iterations. Algorithm 1 below presents a pseudocode of the k-means algorithm [27].…”

Section: ) K-means Clustering Algorithmmentioning

confidence: 99%

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Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Nanfak

Eke

Meghnefi

et al. 2023

IEEE Trans. Dielect. Electr. Insul.

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Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29 compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

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Section: ) K-means Clustering Algorithmmentioning

confidence: 99%

“…Based on their fitness, the selection operator selects a part of the current population for the next iteration. The pseudo-code of the GA-based evolutionary k-MCA is presented in Algorithm 2 [27]. The parameters of this algorithm are given in Table IV.…”

Section: ) Ga-based Evolutionary (K-mca)mentioning

confidence: 99%

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Nanfak

Eke

Meghnefi

et al. 2023

IEEE Trans. Dielect. Electr. Insul.

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“…Addressing this challenge requires solutions that balance computational efficiency without compromising the quality of clustering results. Recent advancements, like evolutionary clustering algorithms, showcase promise in handling large-scale datasets Aljarah et al, 2021). (Carnein & Trautmann, 2019;.…”

Section: Scalability In Big Data Clusteringmentioning

confidence: 99%

Developed Clustering Algorithms for Engineering Applications: A Review

Zangana,

Abdulazeez

2023

Int. J. Inform. Inf. Sys. and Comp. Eng.

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Clustering algorithms play a pivotal role in the field of engineering, offering valuable insights into complex datasets. This review paper explores the landscape of developed clustering algorithms with a focus on their applications in engineering. The introduction provides context for the significance of clustering algorithms, setting the stage for an in-depth exploration. The overview section delineates fundamental clustering concepts and elucidates the workings of these algorithms. Categorization of clustering algorithms into partitional, hierarchical, and density-based forms lay the groundwork for a comprehensive discussion. The core of the paper delves into an extensive review of clustering algorithms tailored for engineering applications. Each algorithm is scrutinized in dedicated subsections, unraveling their specific contributions, applications, and advantages. A comparative analysis assesses the performance of these algorithms, delineating their strengths and limitations. Trends and advancements in the realm of clustering algorithms for engineering applications are thoroughly examined. The review concludes with a reflection on the challenges faced by existing clustering algorithms and proposes avenues for future research. This paper aims to provide a valuable resource for researchers, engineers, and practitioners, guiding them in the selection and application of clustering algorithms for diverse engineering scenarios.

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“…Document clustering can be used in document sorting, document retrieval, data visualization, document analysis, and document tag clustering, etc. [6]. Clustering algorithms are used for finding groups having a high degree of similarity based on maximum similar words among the documents [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Clustering Approach Based on Black Hole Algorithm for Document Clustering

Malik

Khan

2022

IEEE Access

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In information retrieval and text mining, document clustering is a big challenge because the amount of document collection has been increasing, day by day. The problem of clustering is NP-hard, use of meta-heuristic algorithms to solve these problems could be an effective method. When the solution space is large, traditional methods are unable to find a solution in a reasonable amount of time. K-means is a heuristic clustering algorithm, two main issues with heuristic algorithms are the early convergence and trapping in local optima. Moreover, finding the right number of clusters is one of the main drawbacks of the k-means algorithm. The correct value of k is always confusing, different researchers used different methods to solve this problem. To overcome these mentioned problems, this study presents a novel Hybrid approach for document clustering. One of the challenges in existing BH algorithm is the input data type. Recently, the algorithm was only accepting textual data. Another flaw in the existing model is that it doesn't choose how many clusters k to form automatically, and the centroids are chosen at random in it. In this paper, we have constructed a Hybrid cluster identification approach which consists of the Elbow method and Silhouette score for cluster k identification. This paper mainly offers three novel combination of model to represent text documents, namely i) K-mean++ -BH + TF-IDF with fix k ii) K-mean++ -BH + W2V with fix k iii) Hybrid Black Hole with automated k. The proposed improvements have validated on the document clustering problem. Cluster analysis based on two evaluation measures, external (Purity) and internal measures (Silhouette score) are used to report the findings. Experiments have been carried out on the four al-phanumeric datasets (Doc50, Reuters, WebKB and News20) as well as on two numeric datasets (Iris and Wine) respectively. The complete result analysis is reported in detail with respect to each research contribution to compare the performance of the proposed algorithm with existing clustering methods. Result shows that the proposed Hybrid BH algorithm outperforms better than the existing clustering methods for all datasets. The clustering of data with and without stop words is examined; additionally, the two alternative word embedding used for data exploration in conjunction with proposed model are also evaluated. In the present study, proposed Hybrid BH algorithm handles the optimal value of k efficiently. This is one of the major contributions of the paper, concluded that Hybrid Black Hole is an effective algorithm for cluster analysis.

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Introduction to Evolutionary Data Clustering and Its Applications

Cited by 3 publications

References 69 publications

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Developed Clustering Algorithms for Engineering Applications: A Review

A Novel Hybrid Clustering Approach Based on Black Hole Algorithm for Document Clustering

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