An improved K-medoids clustering approach based on the crow search algorithm

Sureja, Nitesh M.; Chawda, Bharat; Vasant, Avani R.

doi:10.1016/j.jcmds.2022.100034

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…If a point is found with the bestimproved value of distortion function, the new data point will replace the current best data point. These newly generated best data points form the new medoids (40)(41)(42).…”

Section: Discussionmentioning

confidence: 99%

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

Huang¹,

Yan²,

Song³

et al. 2023

Quant Imaging Med Surg

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Background: To develop an unsupervised anomaly detection method to identify suspicious error-prone treatment plans in radiotherapy. Methods: A total of 577 treatment plans of breast cancer patients were used in this study. They were labeled as either normal or abnormal plans by experienced clinicians. Multiple features of each plan were extracted and selected by the learning algorithms. The training set consisted of feature samples from 400 normal plans and the testing set consisted of feature samples from 158 normal plans and 19 abnormal plans. Using the k-means clustering algorithm in the training stage, 4 normal plan clusters were formed. The distance between the samples in the testing set and the cluster centers were then determined. To evaluate the effect of dimensionality reduction (DR) on detection accuracy, principal component analysis (PCA) and autoencoder (AE) methods were compared. Results: The sensitivity of the anomaly detection model based on PCA and AE methods were 84.2% (16/19) and 94.7% (18/19), respectively. The specificity of the anomaly detection model based on PCA and AE methods were 64.6% (102/158) and 69.0% (109/158), respectively. The areas under the receiver operating characteristic (ROC) curve (AUCs) based on PCA and AE methods were 0.81 and 0.90, respectively. Conclusions: The unsupervised learning method was effective for detecting anomalies from the feature samples. Accuracy could be improved with the introduction of AE-based DR technique. The combination of AE and k-means clustering methods provides an automated way to identify abnormal plans among clinical treatment plans in radiotherapy.

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Section: Discussionmentioning

confidence: 99%

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

Huang¹,

Yan²,

Song³

et al. 2023

Quant Imaging Med Surg

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show abstract

“…K-medoids, known as Partitioning Arounds Medoids (PAM), is one of the partitioning methods because it uses the medoid (median) as the center point of a cluster [17]. The advantage of k medoids is that the clustering results do not depend on the order of the data [18].…”

Section: K-medoidsmentioning

confidence: 99%

Clustering Provinces with Drought Risk Based on Daily Maximum Temperature

2024

IJESD

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Changes in global weather patterns are sweeping the world, including Indonesia. One of the causes of this change was the El Niño event, where sea surface temperatures in the central Pacific Ocean experienced an increase. Apart from causing temperatures to increase, it also causes the intensity of rainfall to decrease, causing drought disasters. Anticipating natural disasters and disaster mitigation needs to be carried out to reduce their negative impacts. Efforts can be made by identifying areas with a high potential for drought and clustering areas based on the level of potential drought. This article focuses on extreme data from maximum temperatures in 34 provinces in Indonesia. Clustering was performed using the k-means and k-medoids methods and evaluated using the Davies-Bouldin index. Predict the highest maximum temperature in a specific period using the return level. The result shows that the k-means method is more suitable and better implemented by checking on the Davies-Boulding index, which is 0.9945.

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“…Metode K-Medoids clustering merupakan metode nonhierarchical clustering dengan medoid sebagai pusat kelompoknya. Medoid dapat mewakili pusat cluster yang sebenarnya karena ketahanannya terhadap outlier dan noise [18]. Algoritma pengelompokan pada metode K-Medoids clustering adalah sebagai berikut [9]…”

Section: K-medoids Clusteringunclassified

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

Anugrahayu,

Azmi

2023

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The tendency of investors to choose investments with maximum return and minimal risk causes the need for diversification in a portfolio to form an optimal portfolio. A lot of research on stock portfolio optimization has been conducted extensively, but not many have tried to apply machine learning concepts such as clustering analysis to accelerate the establishment of a model that can have a positive effect on the time and cost efficiency of portfolio management. However, clustering is only limited to determining the optimal stock candidate, so it is necessary to add another optimization model to calculate the portfolio weight. Based on these problems, this study carried out portfolio optimization using Mean-Variance (MV) and Mean Absolute Deviation (MAD) model based on K-Medoids Clustering by Dynamic Time Warping approach using Monte Carlo-Expected Tail Loss for risk analysis. Based on the analysis results, the MAD portfolio is more optimal than the MV portfolio by the MAD portfolio consists of five stocks, namely BMRI shares with a weight of 0.06243, UNTR shares of 0.08658, BBRI shares of 0.10285, BBCA of 0.53623, and KLBF shares of 0.21191 are the best optimal portfolios. The optimal portfolio of the MAD model has a rate of return of 87.836% in May 2017 - December 2022 with a portfolio performance of 0.03704, while the resulting risk level based on Carlo-Expected Tail Loss is 2.2416%.

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An improved K-medoids clustering approach based on the crow search algorithm

Cited by 15 publications

References 12 publications

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

Combining autoencoder with clustering analysis for anomaly detection in radiotherapy plans

Clustering Provinces with Drought Risk Based on Daily Maximum Temperature

Stock Portfolio Optimization Using Mean-Variance and Mean Absolute Deviation Model Based On K-Medoids Clustering by Dynamic Time Warping

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