A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation

Kapoor, Sahil; Zeya, Irshad; Singhal, Chirag; Nanda, Satyasai Jagannath

doi:10.1016/j.procs.2017.09.100

Cited by 66 publications

(22 citation statements)

References 16 publications

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“…Real life datasets Point symmetry-based cluster validity index Kapoor et al [102] Cluster analysis and image segmentation…”

Section: Representative Algorithm Clustering Networkmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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The application of several swarm intelligence and evolutionary metaheuristic algorithms in data clustering problems has in the past few decades gained wide popularity and acceptance due to their success in solving and finding good quality solutions to a variety of complex real-world optimization problems. Clustering is considered one of the most important data analysis techniques in the domain of data mining. A clustering problem refers to the partitioning of unlabeled data objects into a certain number of clusters based on their attribute values or features, with the objective of maximizing intra-clusters homogeneity and inter-cluster heterogeneity. This paper presents an up-to-date survey of major nature-inspired metaheuristic algorithms that have been employed to solve automatic clustering problems. Further, a comparative study of several modified well-known global metaheuristic algorithms is carried out to solve automatic clustering problems. Also, three hybrid swarm intelligence and evolutionary algorithms, namely, particle swarm differential evolution algorithm, firefly differential evolution algorithm and invasive weed optimization differential evolution algorithm, are proposed to deal with the task of automatic data clustering. In contrast to many of the existing traditional and evolutionary computational clustering techniques, the clustering algorithms presented in this paper do not require any predetermined information or prior-knowledge of the dataset that is to be classified, but rather they are capable of spontaneously identifying the optimal number of partitions of the data points during the course of program execution. Forty-one benchmarked datasets that comprise eleven artificial and thirty real world datasets are collated and utilized to evaluate the performances of the representative nature-inspired clustering algorithms. According to the extensive experimental results, comparisons and statistical significance, the firefly algorithm appeared to be more appropriate for better clustering of both low and high dimensional data objects than were other state-of-the-art algorithms. Further, an experimental study demonstrates the superiority of the three proposed hybrid algorithms over the standard stateof-the-art methods in finding meaningful clustering solutions to the problem at hand.

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“…Real life datasets Point symmetry-based cluster validity index Kapoor et al [102] Cluster analysis and image segmentation…”

Section: Representative Algorithm Clustering Networkmentioning

confidence: 99%

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Ezugwu

2020

SN Appl. Sci.

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“…To show the advantage of using our proposed clustering method, different clustering techniques such as K‐mean, partitioning around medoids, clustering for large applications, and clustering large applications based on randomized search, which were proposed by Saxena et al (), are run along with SOM. Table compares these clustering techniques considering Davies–Bouldin index (see Kapoor, Zeya, Singhal, & Nanda, ) that is based on dispersion measure within each cluster i (i.e., S i ) and cluster dissimilarity between two clusters i and j (i.e., D ij ). Both criteria are according to Minkowski distance.…”

Section: Case Studymentioning

confidence: 99%

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

et al. 2019

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To assess sustainability of power plants, this paper presents a novel hybrid method. To this end, self‐organizing map method of artificial neural networks is employed. Then, a double frontier data envelopment analysis is developed to rank power plants in each cluster of decision‐making units. Because outputs of power plants might be uncertain, a robust optimization approach is incorporated into proposed double frontier data envelopment analysis model to present ranks that are robust against different uncertainties. A case study is given to validate the proposed model. The case study shows that the proposed model can present improvement solutions that guide power plants towards efficient frontier and far from inefficient frontier. Given the results, decision makers can decide on which power plants should be closed and which power plants should be expanded.

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“…= 0.2, = 1.5, = 0.01, population [22] is the number of Fitness Evaluations (FEs), and the maximum number of FEs (i.e. MAX_FE) has been taken as 1000 × .…”

Section: Parameter Settingmentioning

confidence: 99%

“…K-means is the well-known clustering techniques but sensitive to initial cluster centres and easy convergences to local optimization. Therefore, Nature-Inspired Optimization Algorithms (NIOA) are successfully employed to overcome the problems of K-means in image clustering domain [19][20][21][22][23]. For example, Orman et.…”

Section: Introductionmentioning

confidence: 99%

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

2018

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A Grey Wolf Optimizer Based Automatic Clustering Algorithm for Satellite Image Segmentation

Cited by 66 publications

References 16 publications

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

Nature-inspired metaheuristic techniques for automatic clustering: a survey and performance study

A robust hybrid artificial neural network double frontier data envelopment analysis approach for assessing sustainability of power plants under uncertainty

StuCoSReC. Proceedings of the 2018 5th Student Computer Science Research Conference.

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