SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

Nozad, Sayyed Ahmad Naghavi; Haeri, Maryam Amir; Folino, Gianluigi

doi:10.1016/j.knosys.2021.107256

Cited by 7 publications

(3 citation statements)

References 120 publications

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“…The abnormal data refer to outliers with unreasonable values in the dataset, which has the characteristic that the proportion in the whole dataset is usually small and deviates from the whole. Commonly used abnormal data detection algorithms include clustering-based outlier detection [36][37][38], density-based outlier detection [39,40], and so on. In this paper, we choose the isolated forest algorithm [41], which divides the dataset by constructing a binary tree, expresses the degree of alienation from the data subject according to the depth of the data samples in the binary tree, and finally divides the anomalous data by the anomaly score.…”

Section: Abnormal Data Handlingmentioning

confidence: 99%

Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means

Li,

et al. 2023

Agriculture

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Efficiently managing temperature and humidity in a pig house is crucial for enhancing animal welfare. This research endeavors to develop an intelligent temperature and humidity control system grounded in a three-way decision and clustering algorithm. To establish and validate the effectiveness of this intelligent system, experiments were conducted to compare its performance against a naturally ventilated pig house without any control system. Additionally, comparisons were made with a threshold-based control system to evaluate the duration of temperature anomalies. The experimental findings demonstrate a substantial improvement in temperature regulation within the experimental pig house. Over a 24 h period, the minimum temperature increased by 4 °C, while the maximum temperature decreased by 8 °C, approaching the desired range. Moreover, the average air humidity decreased from 73.4% to 68.2%. In summary, this study presents a precision-driven intelligent control strategy for optimizing temperature and humidity management in pig housing facilities.

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Section: Abnormal Data Handlingmentioning

confidence: 99%

Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means

Li,

et al. 2023

Agriculture

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“…On the other hand, the distance-based techniques include the k-Nearest Neighbor [150] and the Clustering k-Means [151]. These methods assume tightly grouping, as clusters, for normal data, but different data are located far respect to their nearest neighbors.…”

Section: Techniques That Could Be Possible Potential Solutions To The...mentioning

confidence: 99%

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

et al. 2022

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The electric machines are the elements most used at an industry level, and they represent the major power consumption of the productive processes. Particularly speaking, among all electric machines, the motors and their drives play a key role since they literally allow the motion interchange in the industrial processes; it could be said that they are the medullar column for moving the rest of the mechanical parts. Hence, their proper operation must be guaranteed in order to raise, as much as possible, their efficiency, and, as consequence, bring out the economic benefits. This review presents a general overview of the reported works that address the efficiency topic in motors and drives and in the power quality of the electric grid. This study speaks about the relationship existing between the motors and drives that induces electric disturbances into the grid, affecting its power quality, and also how these power disturbances present in the electrical network adversely affect, in turn, the motors and drives. In addition, the reported techniques that tackle the detection, classification, and mitigations of power quality disturbances are discussed. Additionally, several works are reviewed in order to present the panorama that show the evolution and advances in the techniques and tendencies in both senses: motors and drives affecting the power source quality and the power quality disturbances affecting the efficiency of motors and drives. A discussion of trends in techniques and future work about power quality analysis from the motors and drives efficiency viewpoint is provided. Finally, some prompts are made about alternative methods that could help in overcome the gaps until now detected in the reported approaches referring to the detection, classification and mitigation of power disturbances with views toward the improvement of the efficiency of motors and drives.

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“…Clustering is a fundamental technique in data mining and machine learning, aiming to group objects into distinct clusters [ 1 – 7 ]. Objects within a cluster show high similarity to each other and low similarity to objects in other clusters, determined by a similarity measure [ 8 – 11 ].…”

Section: Introductionmentioning

confidence: 99%

An inversion-based clustering approach for complex clusters

Barati Jozan,

Lotfata,

Hamilton

et al. 2024

BMC Res Notes

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Background The choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications. Methods In this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index. Results The simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains. Conclusions The inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

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SDCOR: Scalable density-based clustering for local outlier detection in massive-scale datasets

Cited by 7 publications

References 120 publications

Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means

Smart Temperature and Humidity Control in Pig House by Improved Three-Way K-Means

Advances in Power Quality Analysis Techniques for Electrical Machines and Drives: A Review

An inversion-based clustering approach for complex clusters

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