{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T22:28:20Z","timestamp":1781216900338,"version":"3.54.1"},"reference-count":28,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,6,21]]},"abstract":"<jats:p>Spatial clustering is one of the main techniques for spatial data mining and spatial data analysis. However, existing spatial clustering methods primarily focus on points distributed in planar space with the Euclidean distance measurement. Recently, NS-DBSCAN has been developed to perform clustering of spatial point events in Network Space based on a well-known clustering algorithm, named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The NS-DBSCAN algorithm has efficiently solved the problem of clustering network constrained spatial points. When compared to the NC_DT (Network-Constraint Delaunay Triangulation) clustering algorithm, the NS-DBSCAN algorithm efficiently solves the problem of clustering network constrained spatial points by visualizing the intrinsic clustering structure of spatial data by constructing density ordering charts. However, the main drawback of this algorithm is when the data are processed, objects that are not specifically categorized into types of clusters cannot be removed, which is undeniably a waste of time, particularly when the dataset is large. In an attempt to have this algorithm work with great efficiency, we thus recommend removing edges that are longer than the threshold and eliminating low-density points from the density ordering table when forming clusters and also take other effective techniques into consideration. In this paper, we develop a theorem to determine the maximum length of an edge in a road segment. Based on this theorem, an algorithm is proposed to greatly improve the performance of the density-based clustering algorithm in network space (NS-DBSCAN). Experiments using our proposed algorithm carried out in collaboration with Ho Chi Minh City, Vietnam yield the same results but shows an advantage of it over NS-DBSCAN in execution time.<\/jats:p>","DOI":"10.3233\/jifs-202806","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T14:05:36Z","timestamp":1620741936000},"page":"11653-11670","source":"Crossref","is-referenced-by-count":7,"title":["A method for efficient clustering of spatial data in network space"],"prefix":"10.1177","volume":"40","author":[{"given":"Trang T.D.","family":"Nguyen","sequence":"first","affiliation":[{"name":"Faculty of Information Technology, Nha Trang University, Nha Trang, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loan T.T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, International University, Ho Chi Minh City, Vietnam"},{"name":"Vietnam National University, Ho Chi Minh City, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anh","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Applied Informatics, Wroclaw University of Science and Technology, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Unil","family":"Yun","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Sejong University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bay","family":"Vo","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JIFS-202806_ref1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.4018\/ijdwm.2014100103","article-title":"Spatial data mining: A perspective of big data","volume":"10","author":"Wang","year":"2014","journal-title":"International Journal of Data Warehousing and Mining (IJDWM)"},{"issue":"5","key":"10.3233\/JIFS-202806_ref3","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3390\/ijgi8050218","article-title":"NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space","volume":"8","author":"Wang","year":"2019","journal-title":"International Journal of Geo-Information"},{"issue":"1","key":"10.3233\/JIFS-202806_ref4","first-page":"111","article-title":"Time seriers trend analysis based K-means and support vector machine","volume":"35","author":"Vo","year":"2016","journal-title":"Computing and Informatics"},{"issue":"2","key":"10.3233\/JIFS-202806_ref5","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","article-title":"A simple and fast algorithm for K-medoids clustering","volume":"36","author":"Park","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-202806_ref6","doi-asserted-by":"crossref","unstructured":"Zhang T. , Ramakrishnan R. and Livny M. , BIRCH: An efficient data clustering method for very large databases, ACM SIGMOD 25(2) (1996).","DOI":"10.1145\/235968.233324"},{"issue":"1","key":"10.3233\/JIFS-202806_ref7","first-page":"35","article-title":"CURE: an efficient clustering algorithm for large databases","volume":"26","author":"Guha","year":"2001","journal-title":"ACM SIGMOD"},{"issue":"5","key":"10.3233\/JIFS-202806_ref8","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TKDE.2002.1033770","article-title":"CLARANS: A Method for Clustering Objects for Spatial Data Mining","volume":"14","author":"Ng","year":"2002","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-202806_ref10","doi-asserted-by":"crossref","unstructured":"Ankerst M. , Breunig M.M. , Kriegel H.-P. and Sander J. , OPTICS: ordering points to identify the clustering structure, ACM SIGMOD 28(2) (1999).","DOI":"10.1145\/304181.304187"},{"key":"10.3233\/JIFS-202806_ref11","unstructured":"Hinneburg, Alexander, Gabriel and Hans-Henning, \u201cDENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation,\u201d in Advances in Intelligent Data Analysis VII, Springer, 2007."},{"issue":"1","key":"10.3233\/JIFS-202806_ref12","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1111\/tgis.12313","article-title":"ADCN: An anisotropic density-based clustering algorithm for discovering spatial point patterns with noise","volume":"22","author":"Mai","year":"2018","journal-title":"Transaction in GIS"},{"key":"10.3233\/JIFS-202806_ref14","first-page":"223","article-title":"Constraint-based clustering by fast search and find of density peaks","volume":"330","author":"Liu","year":"2019","journal-title":"Science Direct"},{"key":"10.3233\/JIFS-202806_ref15","doi-asserted-by":"crossref","first-page":"165963","DOI":"10.1109\/ACCESS.2020.3022954","article-title":"F-DPC: Fuzzy Neighborhood-Based Density Peak Algorithm","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/JIFS-202806_ref16","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1109\/TNNLS.2018.2851979","article-title":"ICFS Clustering With Multiple Representatives","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.3233\/JIFS-202806_ref17","doi-asserted-by":"crossref","unstructured":"Fan F. , Qiu L. and Yuan S. , Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities, Science Direct 107, 2020.","DOI":"10.1016\/j.patcog.2020.107452"},{"key":"10.3233\/JIFS-202806_ref18","doi-asserted-by":"crossref","unstructured":"Flores K.G. and Garza S.E. , Density peaks clustering with gap-based automatic center detection, Knowledge-Based Systems 206 (2020).","DOI":"10.1016\/j.knosys.2020.106350"},{"key":"10.3233\/JIFS-202806_ref21","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1007\/s007780050009","article-title":"WaveCluster: a wavelet-based clustering approach for spatial data","volume":"8","author":"Sheikholeslami","year":"1999","journal-title":"VLDB Journal"},{"key":"10.3233\/JIFS-202806_ref22","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.cageo.2011.12.017","article-title":"A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity","volume":"46","author":"Liu","year":"2012","journal-title":"Computers & Geosciences"},{"issue":"6","key":"10.3233\/JIFS-202806_ref23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/79.543975","article-title":"The Expectation-Maximization Algorithm","volume":"13","author":"Moon","year":"1996","journal-title":"IEEE Signal Processing Magazine"},{"key":"10.3233\/JIFS-202806_ref24","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"37","author":"Kohonen","year":"2013","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-202806_ref25","first-page":"578","article-title":"Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations","volume":"754","author":"Roman","year":"2018","journal-title":"International Conference on Computer Science, Engineering and Education Applications"},{"key":"10.3233\/JIFS-202806_ref32","doi-asserted-by":"crossref","unstructured":"J.-H. C. J.-H. K. J.-H. C. Jeong-Hun Kim, \u201cAA-DBSCAN: an approximate adaptive DBSCAN for finding clusters with varying densities,\u201d The Journal of Supercomputing, p. 142\u2013169, 2017.","DOI":"10.1007\/s11227-018-2380-z"},{"key":"10.3233\/JIFS-202806_ref34","doi-asserted-by":"crossref","unstructured":"Jungnickel and Dieter, \u201cShortest Paths,\u201d in Graphs, Networks and Algorithms. Algorithms and Computation in Mathematics, Springer, 2008, pp. 59\u201395.","DOI":"10.1007\/978-3-540-72780-4_3"},{"issue":"3","key":"10.3233\/JIFS-202806_ref36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3068335","article-title":"DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN","volume":"42","author":"Schubert","year":"2017","journal-title":"ACM Transactions on Database Systems"},{"issue":"17","key":"10.3233\/JIFS-202806_ref37","doi-asserted-by":"crossref","first-page":"4718","DOI":"10.3390\/su11174718","article-title":"Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest","volume":"11","author":"Devkota","year":"2019","journal-title":"Sustainability"},{"issue":"1","key":"10.3233\/JIFS-202806_ref38","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data & Knowledge Engineering"},{"issue":"4","key":"10.3233\/JIFS-202806_ref39","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1068\/b35097","article-title":"How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets","volume":"37","author":"Haklay","year":"2010","journal-title":"Environment and Planning B: Planning and Design"},{"key":"10.3233\/JIFS-202806_ref40","first-page":"53","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"Science Direct"},{"issue":"6191","key":"10.3233\/JIFS-202806_ref41","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-202806","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:01Z","timestamp":1777455721000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-202806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,21]]},"references-count":28,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-202806","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,21]]}}}