Automatic data clustering using nature-inspired symbiotic organism search algorithm

Zhou, Yongquan; Wu, Haizhou; Luo, Qifang; Abdel-Baset, Mohamed

doi:10.1016/j.knosys.2018.09.013

Cited by 79 publications

(28 citation statements)

References 27 publications

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“…Clustering is often used for a variety of fascinating real-world applications such as in marketing, biology, image analysis, libraries, insurance, data mining, medicine, statistical data analysis, community detection, and other fields of science and engineering [5,6,7]. Although cluster analysis was first used in two social sciences domains, namely, anthropology and psychology [8], furthermore, it was also used for trait theory classification in personality psychology by Cattell in early 1943 [8,9]. The method of data clustering has since spread with significant relevance in application to other new research areas such as data science and machine learning.…”

Section: Introductionmentioning

confidence: 99%

“…The k-means algorithm seems to be the most popular among these algorithms. However, the success of the algorithms mentioned above in solving clustering analyses problems highly rely on having predetermined information about the data objects and the initial solution, which in most case can easily lead the algorithms into getting trapped around local optima [14]. These are serious drawbacks that have led data mining researchers to improvise and come up with other effective means of overcoming these defects among which includes the use of several evolutionary and swarm intelligence algorithms to deal with more complex and high dimensional data clustering problems.…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

et al. 2020

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In cluster analysis, the goal has always been to extemporize the best possible means of automatically determining the number of clusters. However, because of lack of prior domain knowledge and uncertainty associated with data objects characteristics, it is challenging to choose an appropriate number of clusters, especially when dealing with data objects of high dimensions, varying data sizes, and density. In the last few decades, different researchers have proposed and developed several nature-inspired metaheuristic algorithms to solve data clustering problems. Many studies have shown that the firefly algorithm is a very robust, efficient and effective nature-inspired swarm intelligence global search technique, which has been successfully applied to solve diverse NP-hard optimization problems. However, the diversification search process employed by the firefly algorithm can lead to reduced speed and convergence rate for large-scale optimization problems. Thus this study investigates the application of four hybrid firefly algorithms to the task of automatic clustering of high density and large-scaled unlabelled datasets. In contrast to most of the existing classical heuristic-based data clustering analyses techniques, the proposed hybrid algorithms do not require any prior knowledge of the data objects to be classified. Instead, the hybrid methods automatically determine the optimal number of clusters empirically and during the program execution. Two well-known clustering validity indices, namely the Compact-Separated and Davis-Bouldin indices, are employed to evaluate the superiority of the implemented firefly hybrid algorithms. Furthermore, twelve standard ground truth clustering datasets from the UCI Machine Learning Repository are used to evaluate the robustness and effectiveness of the algorithms against those of the classical swarm optimization algorithms and other related clustering results from the literature. The experimental results show that the new clustering methods depict high superiority in comparison with existing standalone and other hybrid metaheuristic techniques in terms of clustering validity measures.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

et al. 2020

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“…Zhou et al [17] Cluster analysis Real life and artificial datasets Fitness function evaluation Pacheco et al [104] Cluster analysis Real life datasets SI Elaziz et al [105] Cluster analysis Real life and artificial datasets Dunn index, SI, DB index and Calinski-Harabasz (CH) index Chowdhury and Das [37] Pattern recognition Real life and artificial datasets Huang's accuracy measure Sheng et al [106] Miscellaneous Real life and artificial datasets DB, CH, I-index Zhou et al [107] GPS data based trajectory Real life: Taxi GPS Datasets DB index Agbaje et al [108] Cluster analysis Real life datasets DB and CS indices problem at hand. From this analysis, GA has 887, PSO has 524, DE has 180, FA has 49, and DE has 9 published documents.…”

Section: Real Life Datasets S_dbw Indexmentioning

confidence: 99%

“…The algorithm is very efficient and effective, due specifically to its linear time complexity, but also because the deterministic local search used in its implementation usually converges the algorithm to the nearest local optima. However, in recent years, a few automatic data clustering algorithms have been implemented, most of which are inspired by either natural or physically occurring phenomena, among which can be included genetic algorithm (GA) [11,12], differential evolution (DE) [13], particle swarm optimization (PSO) [14,15], gravitational search algorithm (GSA) [16], symbiotic organisms search (SOS) [17], bee colony optimization algorithm (BCA) [18,19], invasive weed optimization (IWO) [20], and bacterial evolutionary algorithm (BEA) [21]. More detailed discussion of some related algorithm implementations applied to automatic clustering are presented in Sect.…”

Section: Introductionmentioning

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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“…They designed a flood image segmentation application based on their proposed work and got two times better accuracy than the K-means when applied on NASA satellite images of flood affected areas of Chennai. Zhou et al (2018), used the symbiotic organism search (SOS) algorithm for solving clustering problems. The SOS algorithm mimics the interactive behavior of the organisms in nature.…”

Section: Contributionsmentioning

confidence: 99%

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

Thalamala

Janet

Reddy

2019

PeerJ Computer Science

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Nature-inspired algorithms are based on the concepts of self-organization and complex biological systems. They have been designed by researchers and scientists to solve complex problems in various environmental situations by observing how naturally occurring phenomena behave. The introduction of nature-inspired algorithms has led to new branches of study such as neural networks, swarm intelligence, evolutionary computation, and artificial immune systems. Particle swarm optimization (PSO), social spider optimization (SSO), and other nature-inspired algorithms have found some success in solving clustering problems but they may converge to local optima due to the lack of balance between exploration and exploitation. In this paper, we propose a novel implementation of SSO, namely social spider optimization for data clustering using single centroid representation and enhanced mating operation (SSODCSC) in order to improve the balance between exploration and exploitation. In SSODCSC, we implemented each spider as a collection of a centroid and the data instances close to it. We allowed non-dominant male spiders to mate with female spiders by converting them into dominant males. We found that SSODCSC produces better values for the sum of intra-cluster distances, the average CPU time per iteration (in seconds), accuracy, the F-measure, and the average silhouette coefficient as compared with the K-means and other nature-inspired techniques. When the proposed algorithm is compared with other nature-inspired algorithms with respect to Patent corpus datasets, the overall percentage increase in the accuracy is approximately 13%. When it is compared with other nature-inspired algorithms with respect to UCI datasets, the overall percentage increase in the F-measure value is approximately 10%. For completeness, the best K cluster centroids (the best K spiders) returned by SSODCSC were specified. To show the significance of the proposed algorithm, we conducted a one-way ANOVA test on the accuracy values and the F-measure values returned by the clustering algorithms.

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Automatic data clustering using nature-inspired symbiotic organism search algorithm

Cited by 79 publications

References 27 publications

A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

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

A novel variant of social spider optimization using single centroid representation and enhanced mating for data clustering

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