DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark

Chen, Guangsheng; Cheng, Yiqun; Jing, Weipeng

doi:10.1504/ijhpcn.2019.10020624

Cited by 2 publications

(4 citation statements)

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“…Te algorithm uses R * -tree structure to merge the results of local clustering and improve the efciency of obtaining global clusters. In addition, Jing et al [23] applied a new data partitioning and merging method to implement the DBSCAN algorithm for partitioning and clustering based on the Spark framework. Te algorithm introduces KD trees in the data partitioning phase and reduces the number of visits to the dataset by using neighbor queries.…”

Section: State Of the Artmentioning

confidence: 99%

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

Zhang

2023

Advances in Multimedia

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The density-based applied spatial clustering algorithm is an algorithm based on high-density interconnected regions, which discovers class clusters of arbitrary shapes in noisy data sets and is widely used. However, it suffers from slow computation speed due to large-scale disk I/O and clustering bias due to uneven density class clusters and poor parameter search ability. To address these problems, a parallel density clustering algorithm based on an improved fruit fly optimization algorithm and Spark memory iteration is proposed. The proposed algorithm first divides the data grid using an irregular dynamic density region partitioning strategy. Then, a hybrid fruit fly particle swarm algorithm based on a genetic optimization mechanism is proposed to achieve dynamic optimization seeking for parameters in local clustering to improve the clustering effect of local clustering. Finally, the local merging of samples in irregularly bounded grid cells under each partition is achieved by designing a custom clustering merging strategy. The experiments show that the improved algorithm is generally applicable to the clustering of different shape class clusters and larger scale data and has obvious improvement in accuracy and parallel efficiency.

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Section: State Of the Artmentioning

confidence: 99%

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

Zhang

2023

Advances in Multimedia

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“…Розглянемо метод кластеризації DBSCAN (Density-Based Spatial Clustering of Noisy Applications) [5,6]…”

Section: вступunclassified

“…Кластеризація та оброблення даних IoT. У дослідженнях [5,6] висвітлюється важливість алгоритмів кластеризації даних IoT для подальшого аналізу та оброблення. Проте існує проблема в обранні найбільш ефективного алгоритму для конкретних завдань.…”

Section: аналіз літературиunclassified

“…Розподілені обчислення. У дослідженнях [5,[8][9][10] Також відображено зв'язки між кластерами DBSCAN і C-Means, що показує, як ресурси та завдання розподіляються між різними локаціями та видами обчислень [12]. Затримка відповіді (у секундах).…”

Section: аналіз літературиunclassified

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Model of elemental data flow distribution in the internet of things supporting fog platform

Rezanov,

Kuchuk

2023

ITSSI

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The subject of the research is models and methods for optimizing resource and task management in the fog computing environment of the Internet of Things (IoT). The increasing number of connected devices and the volumes of data collected in IoT networks make it essential to improve management systems that ensure the optimal distribution of tasks and resources. Fog computing addresses this challenge by distributing computational tasks closer to data sources and end-users. The goal of this work is to enhance the efficiency of fog computing technologies to achieve optimal task and resource allocation in IoT networks. The main tasks of this work are as follows. Firstly, considering the diverse requirements of computational resources and tasks in IoT, reviewing existing methods and developments in the field is necessary. Secondly, it is essential to investigate and compare clustering methods, particularly DBSCAN and C-Means, for effective resource management. The DBSCAN clustering method enables efficient task distribution based on their location, while the C-Means method allows grouping resources based on their characteristics. The final task involves developing a mathematical model that considers input parameters such as system response, cluster resource requirements, data proximity to processing, etc. This model will enable the analysis of potential scenarios and decision-making regarding the optimal distribution of tasks and resources in the IoT environment. Conclusion. This research aims to solve the urgent problem of managing resources and tasks in the fog IoT environment. A review of existing methods and developments in resource and task management in IoT is conducted. DBSCAN and C-Means clustering methods are compared to determine their effectiveness in resource management. A set-theoretic model is developed that considers various parameters for making optimal decisions on the distribution of tasks and resources. It is established that the use of clustering methods and the developed model help to improve system performance and ensure more efficient use of fog computing resources in the IoT environment.

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DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark

Cited by 2 publications

References 0 publications

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

Large Data Oriented to Image Information Fusion Spark and Improved Fruit Fly Optimization Based on the Density Clustering Algorithm

Model of elemental data flow distribution in the internet of things supporting fog platform

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