Particle swarm clustering fitness evaluation with computational centroids

Raitoharju, Jenni; Samiee, Kaveh; Kıranyaz, Serkan; Gabbouj, Moncef

doi:10.1016/j.swevo.2017.01.003

Cited by 16 publications

(5 citation statements)

References 46 publications

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“…For all algorithms, the same cluster seed and termination conditions are used. The Clustering Fitness (CF) for one of the possible clustering results C, is calculated according to (17) [22]. (17) Where λ is the experiential weight, (0 < λ < 1), S ra (c) is the intra-cluster and Ser(c) is the inter-cluster similarity for the cluster c i .…”

Section: Clustering Fitness Testmentioning

confidence: 99%

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Aljebory¹,

Mohammed²,

Zainal³

2021

csit

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Separating an image into regions according to some criterion is called image segmentation. This paper presents an algorithm that combines the fuzzy k-means (FKM) and fuzzy c-means (FCM) clustering strategies. The proposed algorithm combines the FKM and the FCM algorithms mathematical features, which is referred to as (CFKCM). The FKM and FCM clustering algorithms are adopted to compare the performance and hence evaluate the proposed clustering algorithm. Tests are conducted, and performance parameters are calculated for validation. The comparison and assessment analysis are based on metrics related to the image clustering process, such as the Segmentation Accuracy (SA), Clustering Fitness (CF), and cluster Validity function (V pc and V pe ). A dataset of MR images is used by this research for the application, test, and evaluation of the image clustering. The results for clustering backbone MRI images show that CFKCM algorithm is featured by being more effective, and comparatively independent of the noise, where it can process the "clean" images and noisy images without knowing the type of the noise, which is the most difficult task in image segmentation.

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Section: Clustering Fitness Testmentioning

confidence: 99%

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Aljebory¹,

Mohammed²,

Zainal³

2021

csit

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“…Metaheuristic algorithms use predefined objective function to lead the solution toward the optimal one [50]. The objective function directly affects the quality of the results [55]. Thus, considering the best objective function is very important, and is not an easy task.…”

Section: Introductionmentioning

confidence: 99%

EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

et al. 2021

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EvoCluster is an open source and cross-platform framework implemented in Python language, which includes the most wellknown and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. This paper is an extension to the existing EvoCluster framework in which it includes different distance measures for the objective function, different techniques of detecting the k value, and a user option to consider either supervised or unsupervised datasets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, twelve evaluation measures, more than twenty distance measures, and ten different ways for detecting the k value.

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“…The BPSO is used to solve a wide variety of problems including feature selection (Cervante et al 2012), cryptography algorithms (Jadon et al 2011), optimum switching law of inverter (Wu et al 2010), and classification (Cervantes et al 2005). Several algorithms have also been developed to improve the performance of BPSO that includes modified BPSO which adopts concepts of the genotype-phenotype representation and the mutation operator of genetic algorithms (Lee et al 2008), mutation-based binary particle swarm optimization (M-BPSO) for multiple sequence alignment solving (Long et al 2009), improved binary particle swarm optimization to select the small subset of informative genes (Mohamad et al 2011), density-based particle swarm optimization algorithm for data clustering (Alswaitti et al 2018), Particle Swarm Clustering Fitness Evaluation with Computational Centroids (Raitoharju et al 2017), hybrid binary version of bat and enhanced particle swarm optimization algorithm to solve feature selection problems (Tawhid and Dsouza 2018), hybrid improved BPSO and cuckoo search for review spam detection (Rajamohana and Umamaheswari 2017), and hybrid PSO with grey wolf optimizer (HPSOGWO) (Singh and Singh 2017). In any case, original BPSO (Kennedy 1995) effectively stick into neighborhood optima because of single directional data sharing system by the global best particle in the swarm.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid BPSO–SCA approach for feature selection

Kumar

Bharti

2019

Nat Comput

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Nature is a great source of inspiration for solving complex problems in real-world. In this paper, a hybrid nature-inspired algorithm is proposed for feature selection problem. Traditionally, the real-world datasets contain all kinds of features informative as well as non-informative. These features not only increase computational complexity of the underlying algorithm but also deteriorate its performance. Hence, there an urgent need of feature selection method that select an informative subset of features from high dimensional without compromising the performance of the underlying algorithm. In this paper, we select an informative subset of features and perform cluster analysis by employing a cross breed approach of binary particle swarm optimization (BPSO) and sine cosine algorithm (SCA) named as hybrid binary particle swarm optimization and sine cosine algorithm (HBPSOSCA). Here, we employ a V-shaped transfer function to compute the likelihood of changing position for all particles. First, the effectiveness of the proposed method is tested on ten benchmark test functions. Second, the HBPSOSCA is used for data clustering problem on seven real-life datasets taken from the UCI machine learning store and gene expression model selector. The performance of proposed method is tested in comparison to original BPSO, modified BPSO with chaotic inertia weight (C-BPSO), binary moth flame optimization algorithm, binary dragonfly algorithm, binary whale optimization algorithm, SCA, and binary artificial bee colony algorithm. The conducted analysis demonstrates that the proposed method HBPSOSCA attain better performance in comparison to the competitive methods in most of the cases. Keywords Binary artificial bee colony algorithm Á Binary particle swarm optimization Á Binary dragonfly algorithm Á Binary moth flame optimization Á Binary whale optimization algorithm Á Clustering indices Á Feature selection Á Sine cosine algorithm & Kusum Kumari Bharti

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Particle swarm clustering fitness evaluation with computational centroids

Cited by 16 publications

References 46 publications

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

Enhanced Image Segmentation: Merging Fuzzy K-Means and Fuzzy C-Means Clustering Algorithms for Medical Applications

EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

A novel hybrid BPSO–SCA approach for feature selection

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