Gf-Clust: A Nature-Inspired Algorithm for Automatic Text Clustering

Mohammed, Athraa Jasim; Yusof, Yuhanis; Husni, Husniza

doi:10.32890/jict2016.15.1.3

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…However, estimating the number of clusters using KM is a significant challenge. Therefore, authors in Reference [64] conducted a study to introduce a new clustering technique that takes the Firefly Algorithm for dynamic document clustering, called Gravity Firefly Clustering (GF-CLUST). GF-CLUST can classify the required number of clusters for a given test set, challenging in the clustering of texts.…”

Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

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

et al. 2021

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This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

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Section: Firefly Algorithm (Fa)mentioning

confidence: 99%

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

et al. 2021

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“…This task can be achieved by employing a suitable similarity function that should be maximized/minimized the similarity between the documents clusters [6]. Several researchers have used metaheuristic optimization algorithms to solve the text clustering problem such as Genetic Algorithm [22,23], Particle Swarm Optimizer algorithm [24,25], Cuckoo search [26], Ant colony optimization [27], Artificial bee colony algorithm [28,29], Firefly algorithm [30], Harmony Search [31], and the hybrid metaheuristic approaches [32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

“…The study of [29] utilized the Artificial bee colony algorithm in the text document clustering using the gradient search and the chaotic local search to enhance the exploitation capability of the Artificial bee colony. Moreover, the Firefly algorithm (FA) used in [30] to address dynamic text document clustering using a Gravity Firefly Clustering (GF-CLUST). Other studies utilized the Harmony Search for the text document clustering.…”

Section: Related Workmentioning

confidence: 99%

Solving text clustering problem using a memetic differential evolution algorithm

et al. 2020

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The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

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“…Such examples of past studies are by Tung and Pinnoi (2000) who described a study on waste collection vehicle routing-scheduling problem in Hanoi; Kim et al (2006) who conducted a study on waste collection VRPTW with multiple disposal trips and drivers lunch period; Ombuki-Berman et al Table 1 and Table 2 show that heuristic is the most preferred method in solving TDVRP and waste collection problem. This method also has been widely used in solving other real-life problems such as timetabling (Sultan et al, 2004;Abdul-Rahman et al, 2014), text clustering (Mohammed, Yusof & Husni, 2016) and scheduling (Zulkipli, Ibrahim & Benjamin, 2013;Benjamin, Abdul-Rahman & Engku Abu Bakar, 2013).…”

Section: Waste Collection Vehicle Routing Problemmentioning

confidence: 99%