A robust EM clustering approach: ROBEM

Öner, Yüksel; Bulut, Hasan

doi:10.1080/03610926.2020.1722840

Cited by 3 publications

(4 citation statements)

References 19 publications

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“…The clustering method is compelling in the field of machine learning. Research by Öner and Bulut [32] proposed a new clustering algorithm by combining EM clustering algorithm as well as robust principal component analysis (ROBPCA). Furthermore, the proposed method consists of two stages: 1) Anomalies are detected using the ROBPCA algorithm and 2) Dataset available is clustered using EM clustering algorithm.…”

Section: ) One Class Peeling (Ocp) Methodmentioning

confidence: 99%

“…Following that, the analysis shows three main themes: dimensional reduction approach, machine learning approach, and hybrid approach. In addition, there will be seven sub-themes named PCA [4], Random projection [10], DOBIN [26], Stray algorithm [31], ROBEM [32], DAE-KNN [33] as well as OCP method [34].…”

Section: ) Data Abstraction and Analysismentioning

confidence: 99%

“…The statement is supported by research from Aremu et al [36], which implies that PCA methods are frequently used to overcome the curse of dimensionality. PCA aims to extract all relevant factors from a data set and combine them into new orthogonal variables known as principal components [32]. These are linear combinations of correlated variables with fewer components than the original.…”

Section: ) Principal Component Analysismentioning

confidence: 99%

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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

Suboh

Aziz

Shaharudin

et al. 2023

JOIV : Int. J. Inform. Visualization

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In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within high-dimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies.

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Section: ) One Class Peeling (Ocp) Methodmentioning

confidence: 99%

Section: ) Data Abstraction and Analysismentioning

confidence: 99%

Section: ) Principal Component Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

Suboh

Aziz

Shaharudin

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…In the field of machine learning and data mining, research on clustering has always attracted extensive attention [ 1 , 2 , 3 , 4 ]. Clustering methods are primarily categorized as partition-based, density-based, and hierarchical clustering methods [ 5 , 6 , 7 ]. Partition-based clustering methods classify different samples on the basis of features.…”

Section: Introductionmentioning

confidence: 99%

A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

2023

Entropy

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Since the Fuzzy C-Means algorithm is incapable of considering the influence of different features and exponential constraints on high-dimensional and complex data, a fuzzy clustering algorithm based on non-Euclidean distance combining feature weights and entropy weights is proposed. The proposed algorithm is based on the Fuzzy C-Means soft clustering algorithm to deal with high-dimensional and complex data. The objective function of the new algorithm is modified with the help of two different entropy terms and a non-Euclidean way of computing the distance. The distance calculation formula enhances the efficiency of extracting the contribution of different features. The first entropy term helps to minimize the clusters’ dispersion and maximize the negative entropy to control the clustering process, which also promotes the association between the samples. The second entropy term helps to control the weights of features since different features have different weights in the clustering process. Experiments on real-world datasets indicate that the proposed algorithm gives better clustering results than other algorithms. The experiments demonstrate the proposed algorithm’s robustness by analyzing the parameters’ sensitivity and comparing the computational distance formulas. In summary, the improved algorithm improves classification performance under noisy interference and high-dimensional datasets, increases computational efficiency, performs well in real-world high-dimensional datasets, and encourages the development of robust noise-resistant high-dimensional fuzzy clustering algorithms.

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A Study on the Municipal Waste Management Practices of European Union Countries

Altin

2022

Practice, Progress, and Proficiency in Sustainability

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Industrialization, increasingly crowded cities, and the change in consumption habits have caused municipal waste to be an important part of sustainable development. In this study, the practices of EU countries regarding municipal waste management were evaluated using clustering and data envelopment analysis (DEA). The data set for 2019 was collected from Eurostat and eight variables were identified for the analyses. In the first stage, 28 EU countries were divided into four groups using eight variables and EM algorithm. In the second stage, the efficiency scores of the 28 EU countries' recovery and recycling practices were calculated using DEA. A single input and three outputs were determined for the DEA, and the overall, technical, and scale efficiency values were determined using the output-oriented DEA models. The findings show that countries that are efficient in terms of recovery and recycling practices are in the first and second clusters.

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A robust EM clustering approach: ROBEM

Cited by 3 publications

References 19 publications

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

A Robust and High-Dimensional Clustering Algorithm Based on Feature Weight and Entropy

A Study on the Municipal Waste Management Practices of European Union Countries

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