Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Abualigah, Laith; Gandomi, Amir H.; Elaziz, Mohamed Abd; Hamad, Husam Al; Omari, Mahmoud; Alshinwan, Mohammad; Khasawneh, Ahmad M.

doi:10.3390/electronics10020101

Cited by 82 publications

(32 citation statements)

References 141 publications

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“…is stage is crucial to text clustering [16]. Many scholars have proposed extracting the themes and features of text data by using optimisation algorithms or statistics and achieved good text clustering effects [3,16]. Generally, the preprocessing steps of text data clustering are as follows: (i) tokenisation, (ii) stop words, and (iii) feature vector space.…”

Section: Text Preprocessingmentioning

confidence: 99%

“…Many clustering algorithms have been proposed. ey are mainly divided into three categories, namely, overlapping/ nonexclusive, partitional, and hierarchical [3]. Amongst them, partition-based algorithms are widely used in various fields because of their easy implementation [4].…”

Section: Introductionmentioning

confidence: 99%

“…Meena and Singh [11] used the genetic algorithm and discrete difference evolution to search for the location of the global optima solution whilst reducing the number of algorithm iterations. Abualigah et al [3] comprehensively reviewed the application of optimisation algorithms based on metaheuristics in the field of text clustering. However, the abovementioned algorithms optimise the clustering centres successively through iteration, and algorithm efficiency is not ideal.…”

Section: Introductionmentioning

confidence: 99%

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Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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The K-means algorithm has been extensively investigated in the field of text clustering because of its linear time complexity and adaptation to sparse matrix data. However, it has two main problems, namely, the determination of the number of clusters and the location of the initial cluster centres. In this study, we propose an improved K-means++ algorithm based on the Davies-Bouldin index (DBI) and the largest sum of distance called the SDK-means++ algorithm. Firstly, we use the term frequency-inverse document frequency to represent the data set. Secondly, we measure the distance between objects by cosine similarity. Thirdly, the initial cluster centres are selected by comparing the distance to existing initial cluster centres and the maximum density. Fourthly, clustering results are obtained using the K-means++ method. Lastly, DBI is used to obtain optimal clustering results automatically. Experimental results on real bank transaction volume data sets show that the SDK-means++ algorithm is more effective and efficient than two other algorithms in organising large financial text data sets. The F-measure value of the proposed algorithm is 0.97. The running time of the SDK-means++ algorithm is reduced by 42.9% and 22.4% compared with that for K-means and K-means++ algorithms, respectively.

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Section: Text Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Novel Automated K-means++ Algorithm for Financial Data Sets

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…Many applications have been addressed from user-generated contents in social media such as personal classification based on profiles in social media [3], group decision making [4], aggregation identification [5], domestic violence detection [6,7], clustering analysis [8,9], extremist affiliations [10], prediction and analysis of elections [11][12][13], analysis of happiness patterns [14], education evaluation [15], football events [16], multimedia summarization [17,18], and many other applications [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Fan

Dahou

et al. 2021

Electronics

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Social media has become an essential facet of modern society, wherein people share their opinions on a wide variety of topics. Social media is quickly becoming indispensable for a majority of people, and many cases of social media addiction have been documented. Social media platforms such as Twitter have demonstrated over the years the value they provide, such as connecting people from all over the world with different backgrounds. However, they have also shown harmful side effects that can have serious consequences. One such harmful side effect of social media is the immense toxicity that can be found in various discussions. The word toxic has become synonymous with online hate speech, internet trolling, and sometimes outrage culture. In this study, we build an efficient model to detect and classify toxicity in social media from user-generated content using the Bidirectional Encoder Representations from Transformers (BERT). The BERT pre-trained model and three of its variants has been fine-tuned on a well-known labeled toxic comment dataset, Kaggle public dataset (Toxic Comment Classification Challenge). Moreover, we test the proposed models with two datasets collected from Twitter from two different periods to detect toxicity in user-generated content (tweets) using hashtages belonging to the UK Brexit. The results showed that the proposed model can efficiently classify and analyze toxic tweets.

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“…The method of clustering documents is used [14]. Clustering methods have been improved with regard to the consideration of artificial (AI) [15] techniques such as Particle Swarm Optimization (PSO), Whale Optimization (WOA), and GWO. This paper has the main contributions,…”

Section: Introductionmentioning

confidence: 99%

Type2 IFC with SOA for Topic Detection and Document Clustering Analysis

Perumal¹,

Mathivanan²

2021

Preprint

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The automatic document clustering and topic extraction from the corpus provides a very essential requirement in many real time applications. The document clustering and topic detection is utilized to locating data quickly. Hence, in this paper, Type 2 Intuitionistic Fuzzy Clustering and Seagull Optimization Algorithm (Type 2 IFCSOA) is developed for document clustering and topic detection. The Type 2 IFCSOA is utilized to cluster the documents. Additionally, ensemble approach is utilized to identify by the topics from the clustered documents. In the proposed methodology, the pre-processing is utilized to remove unwanted information from the documents such as tokenization, stop word removal and stemming process. After that, the proposed method is utilized to cluster the documents. The clustered documents are labeled with the basis of clusters. After that, to achieve topic detection, the ensemble approach is utilized with feature extraction phases such as Term Frequency- Inverse Document Frequency (TF-IDF), Mutual information (MI), Text Rank Algorithm and analysis of keyword taking out from co-occurrence statistical -Information (CSI). The proposed methodology is implemented in MATLAB and performances were evaluated with the statistical measurements such as precision, recall, accuracy, sensitivity, purity measure and entropy. The proposed method is compared with the conventional methods such as Fuzzy C Means clustering (FCM), FCM-Particle Swarm Optimization (PSO), FCM-Genetic Algorithm (GA) and K means clustering.

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Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Cited by 82 publications

References 141 publications

Novel Automated K-means++ Algorithm for Financial Data Sets

Novel Automated K-means++ Algorithm for Financial Data Sets

Social Media Toxicity Classification Using Deep Learning: Real-World Application UK Brexit

Type2 IFC with SOA for Topic Detection and Document Clustering Analysis

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