Byoungwook Kim¹, Hong-Jun Jang^2,*

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 has a problem in that… More > Graphic Abstract

R. Krishnaswamy¹, Kamalraj Subramaniam², V. Nandini³, K. Vijayalakshmi⁴, Seifedine Kadry⁵, Yunyoung Nam^6,*

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 by the use of Map… More >

Hang He^1,*, 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 the changes of vulnerability indexes… More >