{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T14:38:03Z","timestamp":1770043083210,"version":"3.49.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>DBSCAN (density-based spatial clustering of applications with noise) is one of the most widely used density-based clustering algorithms, which can find arbitrary shapes of clusters, determine the number of clusters, and identify noise samples automatically. However, the performance of DBSCAN is significantly limited as it is quite sensitive to the parameters of eps and MinPts. Eps represents the eps-neighborhood and MinPts stands for a minimum number of points. Additionally, a dataset with large variations in densities will probably trap the DBSCAN because its parameters are fixed. In order to overcome these limitations, we propose a new density-clustering algorithm called GNN-DBSCAN which uses an adaptive Grid to divide the dataset and defines local core samples by using the Nearest Neighbor. With the help of grid, the dataset space will be divided into a finite number of cells. After that, the nearest neighbor lying in every filled cell and adjacent filled cells are defined as the local core samples. Then, GNN-DBSCAN obtains global core samples by enhancing and screening local core samples. In this way, our algorithm can identify higher-quality core samples than DBSCAN. Lastly, give these global core samples and use dynamic radius based on k-nearest neighbors to cluster the datasets. Dynamic radius can overcome the problems of DBSCAN caused by its fixed parameter eps. Therefore, our method can perform better on dataset with large variations in densities. Experiments on synthetic and real-world datasets were conducted. The results indicate that the average Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), Adjusted Mutual Information (AMI) and V-measure of our proposed algorithm outperform the existing algorithm DBSCAN, DPC, ADBSCAN, and HDBSCAN.<\/jats:p>","DOI":"10.3233\/jifs-211922","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T14:01:29Z","timestamp":1629813689000},"page":"7589-7601","source":"Crossref","is-referenced-by-count":3,"title":["GNN-DBSCAN: A new density-based algorithm using grid and the nearest neighbor"],"prefix":"10.1177","volume":"41","author":[{"given":"Li","family":"Yihong","sequence":"first","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu, China"}]},{"given":"Wang","family":"Yunpeng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu, China"}]},{"given":"Li","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu, China"}]},{"given":"Lan","family":"Xiaolong","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu, China"}]},{"given":"Song","family":"Han","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Sichuan University, Chengdu, China"}]}],"member":"179","reference":[{"issue":"3","key":"10.3233\/JIFS-211922_ref1","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: a review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.3233\/JIFS-211922_ref2","doi-asserted-by":"crossref","first-page":"113689","DOI":"10.1016\/j.eswa.2020.113689","article-title":"STCCD: Semantic trajectory clustering based on community detection in networks","volume":"162","author":"Liu","year":"2020","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/JIFS-211922_ref3","doi-asserted-by":"crossref","first-page":"107420","DOI":"10.1016\/j.patcog.2020.107420","article-title":"Multiple Strong and Balanced Clusters based Ensemble of Deep Learners","author":"Jan","year":"2020","journal-title":"Pattern Recognition"},{"issue":"3","key":"10.3233\/JIFS-211922_ref4","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1348\/135910705X25697","article-title":"The use and reporting of cluster analysis in health psychology: A review","volume":"10","author":"Clatworthy","year":"2005","journal-title":"British Journal of Health Psychology"},{"key":"10.3233\/JIFS-211922_ref5","first-page":"88","article-title":"An isolated virtual cluster for SCADA network security research","volume":"1","author":"Lemay","year":"2013","journal-title":"1st International Symposium for ICS & SCADA Cyber Security Research 2013 (ICS-CSR 2013)"},{"issue":"1","key":"10.3233\/JIFS-211922_ref6","first-page":"1","article-title":"Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms","volume":"40","author":"Al-Andoli","year":"2021","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"key":"10.3233\/JIFS-211922_ref7","doi-asserted-by":"crossref","unstructured":"Al-Andoli M. , Cheah W.P. and Tan S.C. , Deep learning-based community detection in complex networks with network partitioning and reduction of trainable parameters, Journal of Ambient Intelligence and Humanized Computing 3 (2020).","DOI":"10.1007\/s12652-020-02389-x"},{"key":"10.3233\/JIFS-211922_ref8","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.knosys.2013.01.030","article-title":"High-order fuzzy-neuro expert system for time series forecasting","volume":"46","author":"Singh","year":"2013","journal-title":"Knowledge-Based Systems"},{"issue":"10","key":"10.3233\/JIFS-211922_ref9","doi-asserted-by":"crossref","first-page":"2443","DOI":"10.1016\/j.engappai.2013.07.012","article-title":"An efficient time series forecasting model based on fuzzy time series","volume":"26","author":"Singh","year":"2013","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"34","key":"10.3233\/JIFS-211922_ref10","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"Ester","year":"1996","journal-title":"Kdd"},{"key":"10.3233\/JIFS-211922_ref11","first-page":"1","article-title":"MDBSCAN: Multi-level density based spatial clustering of applications with noise","author":"Wang","year":"2016","journal-title":"Proceedings of the The 11th International Knowledge Management in Organizations Conference on The changing face of Knowledge Management Impacting Society"},{"issue":"6191","key":"10.3233\/JIFS-211922_ref12","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"},{"key":"10.3233\/JIFS-211922_ref13","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1016\/j.patcog.2016.07.007","article-title":"Density-ratio based clustering for discovering clusters with varying densities","volume":"60","author":"Zhu","year":"2016","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-211922_ref14","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.engappai.2018.03.014","article-title":"A novel data clustering algorithm using heuristic rules based on k-nearest neighbors chain","volume":"72","author":"Lu","year":"2018","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.3233\/JIFS-211922_ref15","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.neucom.2015.10.020","article-title":"KPRSCAN: A clustering method based on Page Rank","volume":"175","author":"Liu","year":"2016","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-211922_ref16","first-page":"160","article-title":"Densitybased clustering based on hierarchical density estimates","author":"Campello","year":"2013","journal-title":"In Pacific-Asia conference on knowledge discovery and data mining"},{"issue":"11","key":"10.3233\/JIFS-211922_ref17","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1109\/T-C.1973.223640","article-title":"Clustering using a similarity measure based on shared near neighbors","volume":"100","author":"Jarvis","year":"1973","journal-title":"IEEE Transactions on Computers"},{"key":"10.3233\/JIFS-211922_ref18","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/ISSPA.2012.6310472","article-title":"Cmune: A clustering using mutual nearest neighbors algorithm","author":"Abbas","year":"2012","journal-title":"In 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)"},{"issue":"5","key":"10.3233\/JIFS-211922_ref19","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":"3","key":"10.3233\/JIFS-211922_ref20","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.is.2012.09.001","article-title":"Enhancing density-based clustering: Parameter reduction and outlier detection","volume":"38","author":"Cassisi","year":"2013","journal-title":"Information Systems"},{"key":"10.3233\/JIFS-211922_ref21","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2015.05.109","article-title":"An efficient and scalable densitybased clustering algorithm for datasets with complex structures","volume":"171","author":"Lv","year":"2016","journal-title":"Neurocomputing"},{"key":"10.3233\/JIFS-211922_ref22","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1109\/ICDM.2006.9","article-title":"A simple yet effective data clustering algorithm","author":"Vadapalli","year":"2006","journal-title":"In Sixth International Conference on Data Mining (ICDM\u201906)"},{"issue":"6","key":"10.3233\/JIFS-211922_ref23","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/TKDE.2017.2787640","article-title":"RNN-DBSCAN: A densitybased clustering algorithm using reverse nearest neighbor density estimates","volume":"30","author":"Bryant","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/JIFS-211922_ref24","doi-asserted-by":"crossref","first-page":"107206","DOI":"10.1016\/j.patcog.2020.107206","article-title":"A novel density-based clustering algorithm using nearest neighbor graph","volume":"102","author":"Li","year":"2020","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.3233\/JIFS-211922_ref25","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.3233\/JIFS-211922_ref26","first-page":"986","article-title":"KNN model-based approach in classification","author":"Guo","year":"2003","journal-title":"OTM Confederated International Conferences \u201cOn the Move to Meaningful Internet Systems\u201d"},{"key":"10.3233\/JIFS-211922_ref27","doi-asserted-by":"crossref","unstructured":"Chowdhury S. and Amorim R. , An efficient densitybased clustering algorithm using reverse nearest neighbour, (2018).","DOI":"10.1007\/978-3-030-22868-2_3"},{"issue":"2","key":"10.3233\/JIFS-211922_ref28","doi-asserted-by":"crossref","first-page":"105317","DOI":"10.1016\/j.cmpb.2020.105317","article-title":"A neutrosophic-entropy based clustering algorithm (NEBCA) with HSV color system: A special application in segmentation of Parkinson\u2019s disease (PD) MR images","volume":"189","author":"Singh","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.3233\/JIFS-211922_ref29","doi-asserted-by":"crossref","unstructured":"Khan G.A. , et al., Multi-view data clustering via nonnegative matrix factorization with manifold regularization, International Journal of Machine Learning and Cybernetics 2 (2021).","DOI":"10.1007\/s13042-021-01307-7"},{"key":"10.3233\/JIFS-211922_ref30","doi-asserted-by":"crossref","unstructured":"Khan G.A. , et al., Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization, 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) IEEE (2019).","DOI":"10.1109\/ISKE47853.2019.9170204"},{"issue":"3","key":"10.3233\/JIFS-211922_ref31","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1214\/13-AOS1129","article-title":"Quantile and quantile-function estimations under density ratio model[J]","volume":"41","author":"Chen","year":"2013","journal-title":"Annals of Statistics"},{"issue":"9","key":"10.3233\/JIFS-211922_ref32","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1145\/361002.361007","article-title":"Multidimensional Binary Seareh Trees Used for Assoeiative Searehing","volume":"18","author":"Beniley","year":"1975","journal-title":"ACM Communications"},{"key":"10.3233\/JIFS-211922_ref33","unstructured":"Kriegel N.B.H.P. , Schneider R. and Seeger B. , The R*-tree: An E cient and Robust Access Method for Points and Rectangles. In Proceedings of the ACM SIGMOD Conference on Management of Data, (1990)."},{"key":"10.3233\/JIFS-211922_ref34","doi-asserted-by":"crossref","unstructured":"Lipton R.J. , The P= NP Question and G\u00f6del\u2019s Lost Letter. Springer Science & Business Media, (2010).","DOI":"10.1007\/978-1-4419-7155-5"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211922","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T02:57:23Z","timestamp":1770001043000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211922"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":34,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211922","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}