Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Aggarwal, Shruti; Singh, Paramvir

doi:10.1111/exsy.12353

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…It has the advantages of simple operation and high scalability and compressibility in the processing of large data sets. However, the algorithm also has certain limitations: (a) sensitive to cluster centres, (b) susceptible to outliers and (c) no criterion for the choice of k value to be equal (Aggarwal & Singh, 2019). This paper embodies the contribution of feature attributes to clustering by assigning different weights to each attribute in each cluster.…”

Section: Methodsmentioning

confidence: 99%

Retyping of triple‐negative breast cancer based on clustering method

Liu

Wang

et al. 2020

Expert Systems

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Triple‐negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple‐negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple‐negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple‐negative breast cancer, and the results show that the triple‐negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple‐negative breast cancer have been obtained. These genes have the following three characteristics: co‐expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis.

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Section: Methodsmentioning

confidence: 99%

Retyping of triple‐negative breast cancer based on clustering method

Liu

Wang

et al. 2020

Expert Systems

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“…Although the objective function incorporated into these algorithms is formulated to guarantee convergence to a local optimum, there is no guarantee that the convergence result will be accurate. This is because the algorithms can be trapped in a local optimum (Aggarwal & Singh, 2019; Qin et al, 2016).…”

Section: Pixel Classification Techniquesmentioning

confidence: 99%

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

et al. 2022

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Currently, no classical clustering algorithm is efficient on its own. The predefined number of clusters required for their operation does not consistently produce satisfactory segmentation results. They exhibit cluster instability, are vulnerable to the local optimum trap, and are sensitive to noise and imaging artefacts. Most contributions designed to overcome these drawbacks incorporate prior knowledge such as cluster label information and statistic measures that demand minimal labelled training data. Although these approaches improve the segmentation accuracy, they tend to diminish the advantages of clustering algorithms over the supervised learning methods. This study proposes a shift from the use of a predefined number of clusters to a clustering tree-based method for performance enhancement of classical clustering algorithms. The proposed method is a three-stage algorithm. It begins with the extraction of low-level features from a clustering tree. Clustering trees are sets of labelled clusters of an image at multiple clustering resolutions. The second stage extracts high-level features by coupling the clustering tree to a single-layer feedforward neural network. The third stage is the classification stage, where the basic model of a neural network extracts the tumour from a high-level feature map.Because neither of the neural networks requires training, the proposed method is both fully unsupervised and fully automated and retains all its advantages over supervised methods. A performance evaluation using FLAIR MRI images of brain tumour patients from the BRATS2015 and BRATS2020 databases demonstrates significant performance enhancement over four classical clustering algorithms and two of the four proposed techniques were comparable to deep learning methods.

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“…Case 2 was a real-world dataset concerning credit risk assessment. When a borrower or trader is unwilling or unable to fulfil the contract conditions, the banks, investors, or counterparties may suffer monetary losses (Barboza et al, 2017); therefore, credit risk assessment is crucial to identifying the types of borrower or trader and predicting risks (Soui et al, 2019). In case 2, four credit risk assessment datasets were chosen to test the ES-NMPBFO performance.…”

Section: Casesmentioning

confidence: 99%

“…After clustering, the objects in the same group have high similarity, whereas those in different groups have low similarity (Saxena et al, 2017). Whilst a variety of clustering algorithms have been proposed in previous research, none of these clustering algorithms exhibits remarkable advantages in all contexts (Aggarwal & Singh, 2019; Dogan & Birant, 2021; Figueiredo et al, 2019). These algorithms have various design emphases, which depend on several factors, such as the computational complexity, convergence speed, the capability of pre‐setting important parameters (e.g., cluster number, radius), and insensitivity to outliers (Figueiredo et al, 2019; Nanda & Panda, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

Chang

Tang²,

Niu

2021

Expert Systems

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Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets.The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.

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Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Cited by 11 publications

References 18 publications

Retyping of triple‐negative breast cancer based on clustering method

Retyping of triple‐negative breast cancer based on clustering method

Enhancement of clustering techniques by coupling clustering tree and neural network: Application to brain tumour segmentation

Evolutionary state‐based novel multi‐objective periodic bacterial foraging optimization algorithm for data clustering

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