Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm

Ghezelbash, Reza; Maghsoudi, Abbas; Carranza, Emmanuel John M.

doi:10.1016/j.cageo.2019.104335

Cited by 62 publications

(14 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, thanks to the conformal prediction framework, the resulting clustering model has a clear statistical meaning without any assumptions regarding the distribution of the data. Ghezelbash et al [18] believed that, due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, the detection of multi-elemental geochemical footprints of mineral deposits of interest was a challenging task. To address this, a hybrid genetic algorithm-based technique, namely the genetic K-means clustering (GKMC) algorithm, was proposed for the optimum delineation of multi-elemental patterns (both anomaly and background) in stream sediment geochemical data.…”

Section: Clustering-based Methodsmentioning

confidence: 99%

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Liu

2021

Electronics

View full text Add to dashboard Cite

There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data anomaly detection and analysis of multi-sensors is a challenging task: firstly, the data that are collected by different sensors of underground mining are heterogeneous; secondly, real-time is required for the data anomaly detection of safety warning. Currently, there are many anomaly detection methods, such as traditional clustering methods K-means and C-means. Meanwhile, Artificial Intelligence (AI) is widely used in data analysis and prediction. However, K-means and C-means cannot directly process heterogeneous data, and AI algorithms require equipment with high computing and storage capabilities. IoT equipment of underground mining cannot perform complex calculation due to the limitation of energy consumption. Therefore, many existing methods cannot be directly used for IoT applications in underground mining. In this paper, a multi-sensors data anomaly detection method based on edge computing is proposed. Firstly, an edge computing model is designed, and according to the computing capabilities of different types of devices, anomaly detection tasks are migrated to different edge devices, which solve the problem of insufficient computing capabilities of the devices. Secondly, according to the requirements of different anomaly detection tasks, edge anomaly detection algorithms for sensor nodes and sink nodes are designed respectively. Lastly, an experimental platform is built for performance comparison analysis, and the experimental results show that the proposed algorithm has better performance in anomaly detection accuracy, delay, and energy consumption.

show abstract

Section: Clustering-based Methodsmentioning

confidence: 99%

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Liu

2021

Electronics

View full text Add to dashboard Cite

show abstract

“…El-Shorbagy et al [37] proposed an enhanced GA with a new mutation where the K-means algorithm initializes the GA population for finding the best cluster centers. Genetic K-Means clustering (GKMC) was proposed by Ghezelbash, Maghsoudi, and Carranza [38] for optimally delineating multi-elemental patterns in stream sediment geochemical data.…”

Section: Genetic Algorithmmentioning

confidence: 99%

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021

View full text Add to dashboard Cite

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

show abstract

“…ii) Support vector machine (SVM). Miao et al [25] proposed a distributed online one-class [32] Support vector machine Ou et al [33], Babaei et al [34] Neural network Wazid and Das [35] Nearest neighbor KNN Ghezelbash et al [36] Relative density Schmutz et al [37], Krishnaveni et al [38] Clustering Regular clustering Xiang et al [39] Co-clustering Zhai et al [40] SVM algorithm to discover anomalous data via wireless sensor networks and get a decentralized loss function. iii) Neural networks.…”

Section: ) Classificationmentioning

confidence: 99%

Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

Feng

Zhang

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

Driven by industrial development and the rising population, the upward trend of electricity consumption is not going to curb. While the electricity suppliers make every endeavor to satisfy the needs of consumers, they are facing the plight of indirect losses caused by technical or non-technical factors. Technical losses are usually induced by short circuits, power outage, or grid failures. The non-technical losses result from humans’ improper behaviors, e.g., electricity burglars. Due to the restrictions of the detection methods, the detection rate in the traditional power grid is lousy. To provide better electricity service for the customers and minimize the losses for the providers, a leap in the power grid is occurring, which is referred to as the smart grid. The smart grid is envisioned to increase the detection accuracy to an acceptable level by utilizing modern technologies, such as cloud computing. With the aim of obtaining achievements of anomaly detection for electricity consumption with cloud computing, we firstly introduce the basic definition of anomaly detection for electricity consumption. Next, we conduct the surveys on the proposed framework of anomaly detection for electricity consumption and propose a new framework with cloud computing. This is followed by centralized and decentralized detection methods. Then, the applications of centralized and decentralized detection methods for the anomaly electricity consumption are listed. Finally, the open challenges of the accuracy of detection and anomaly detection for electricity consumption with edge computing are discussed.

show abstract

Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm

Cited by 62 publications

References 44 publications

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

Edge Computing for Data Anomaly Detection of Multi-Sensors in Underground Mining

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

Anomaly detection for electricity consumption in cloud computing: framework, methods, applications, and challenges

Contact Info

Product

Resources

About