A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

Li, Zhiyong; Yi, Jiao-Hong; Wang, Gai-Ge

doi:10.3390/a8040951

Cited by 24 publications

(5 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cluster analysis is the task of grouping a set of objects in groups (cluster) in the way that each object in the same cluster are more similar to each other than to those in other clusters [15]. Clustering has been successfully applied in various engineering and scientific disciplines such as biology, medicine, machine learning, pattern recognition, image analysis and data mining.…”

Section: Clustering and Evolutionary Algorithmmentioning

confidence: 99%

Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover

Pitiot¹,

Aldanondo

Vareilles

et al. 2019

2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

View full text Add to dashboard Cite

Section: Clustering and Evolutionary Algorithmmentioning

confidence: 99%

Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover

Pitiot¹,

Aldanondo

Vareilles

et al. 2019

2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

View full text Add to dashboard Cite

“…Another approach for solving the optimization problem in clustering is by considering metaheuristics algorithms such as krill herd (KH) [19]- [21] and hybrid swarm intelligence clustering ensemble (HSICE) [22]. The KH method was constructed based on the best krill individual in the population by Gandomi and Alavi [19], and then Li et al [20] introduced a new version of KH with elitism strategy to improve the parameter estimation and simultaneously solve the optimum global issue in clustering problem. HSICE by Logesh et al [22] combined the BrainStorm optimization algorithm and immune genetic algorithm to generate the diversified list of points of interest.…”

Section: Introductionmentioning

confidence: 99%

Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

2021

View full text Add to dashboard Cite

The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by initializations with a given number of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In this paper, based on several entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that it can be free of initializations without a given number of clusters, and also has a feature reduction behavior. That is, the proposed Entropy-k-means algorithm can eliminate irrelevant features with feature reduction under free of initializations with automatically finding an optimal number of clusters. Comparisons between the proposed Entropy-k-means and other methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed Entropy-k-means with its effectiveness and usefulness in practice.

show abstract

“…The clustering problem is an unsupervised problem, which aims at assigning similar groups together to discover unlabeled similar structures in data without any prior knowledge [1] [2]. Generally, many clustering algorithms have been developed in such a manner that objects in the same cluster should be similar to each other while objects in different clusters should be dissimilar [3] [4]. Clustering algorithms have been widely applied in solving many problems in various fields such as data analysis [5] [6], data mining [7] [8], machine learning [9], and image retrieval [10].…”

Section: Introductionmentioning

confidence: 99%

Automatic Clustering of DNA Sequences With Intelligent Techniques

2021

View full text Add to dashboard Cite

With the discovery of new DNAs, a fundamental problem arising is how to categorize those DNA sequences into correct species. Unfortunately, identifying all data groups correctly and assigning a set of DNAs into k clusters where k must be predefined are one of the major drawbacks in clustering analysis, especially when the data have many dimensions and the number of clusters is too large and hard to guess. Furthermore, finding a similarity measure that preserves the functionality and represents both the composition and distribution of the bases in a DNA sequence is one of the main challenges in computational biology. In this paper, a new soft computing metaheuristic framework is introduced for automatic clustering to generate the optimal cluster formation and to determine the best estimate for the number of clusters. Pulse coupled neural network (PCNN) is utilized for the calculation of DNA sequence similarity or dissimilarity. Bat algorithm is hybridized with the well-known genetic algorithm to solve the automatic data clustering problem. Extensive computational experiments are conducted on the expanded human oral microbiome database (eHOMD). A comparative study between the experimental results shows that the proposed hybrid algorithm achieved superior performance over the standard genetic algorithm and bat algorithm. Moreover, the hybrid performance was compared with competing algorithms from the literature review to ascertain its superiority. Mann-Whitney-Wilcoxon rank-sum test is conducted to statistically validate the obtained clusters.

show abstract

A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy

Cited by 24 publications

References 63 publications

Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover

Product/Process Configuration Evolutionary Optimization: A Multiobjective Clustering in Order to Reduce Inconsistencies During Crossover

Entropy K-Means Clustering With Feature Reduction Under Unknown Number of Clusters

Automatic Clustering of DNA Sequences With Intelligent Techniques

Contact Info

Product

Resources

About