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  • Open Access


    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

    Byoungwook Kim1, Hong-Jun Jang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.2, pp. 1275-1294, 2023, DOI:10.32604/cmes.2022.022446

    Abstract As location information of numerous Internet of Thing (IoT) devices can be recognized through IoT sensor technology, the need for technology to efficiently analyze spatial data is increasing. One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data. In this paper, we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm. The existing DBSCAN algorithm… More > Graphic Abstract

    Genetic-Based Keyword Matching DBSCAN in IoT for Discovering Adjacent Clusters

  • Open Access


    Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

    R. Krishnaswamy1, Kamalraj Subramaniam2, V. Nandini3, K. Vijayalakshmi4, Seifedine Kadry5, Yunyoung Nam6,*

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 391-406, 2023, DOI:10.32604/csse.2023.024901

    Abstract Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved… More >

  • Open Access


    Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

    Hang He1,*, Wanggen Liu1, Zhenhan Zhao1, Shan He1, Jinghui Zhang2

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 643-655, 2022, DOI:10.32604/csse.2022.027211

    Abstract To enhance the accuracy of performance analysis of regional airline network, this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to investigate the topology of regional airline network, constructs node importance index system, and clusters 161 airport nodes of regional airline network. Besides, entropy power method and approximating ideal solution method (TOPSIS) is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points; adopt network efficiency, maximum connectivity subgraph and network connectivity as vulnerability measurement indexes, and observe… More >

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