{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T16:10:29Z","timestamp":1781021429065,"version":"3.54.1"},"reference-count":36,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2021,10,29]]},"abstract":"<jats:p>Most density-based clustering algorithms have the problems of difficult parameter setting, high time complexity, poor noise recognition, and weak clustering for datasets with uneven density. To solve these problems, this paper proposes FOP-OPTICS algorithm (Finding of the Ordering Peaks Based on OPTICS), which is a substantial improvement of OPTICS (Ordering Points To Identify the Clustering Structure). The proposed algorithm finds the demarcation point (DP) from the Augmented Cluster-Ordering generated by OPTICS and uses the reachability-distance of DP as the radius of neighborhood eps of its corresponding cluster. It overcomes the weakness of most algorithms in clustering datasets with uneven densities. By computing the distance of the k-nearest neighbor of each point, it reduces the time complexity of OPTICS; by calculating density-mutation points within the clusters, it can efficiently recognize noise. The experimental results show that FOP-OPTICS has the lowest time complexity, and outperforms other algorithms in parameter setting and noise recognition.<\/jats:p>","DOI":"10.3233\/ida-205497","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T16:08:37Z","timestamp":1635869317000},"page":"1453-1471","source":"Crossref","is-referenced-by-count":22,"title":["An improved OPTICS clustering algorithm for discovering clusters with uneven densities"],"prefix":"10.1177","volume":"25","author":[{"given":"Chunhua","family":"Tang","sequence":"first","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Foreign Languages, Shanghai Maritime University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiwen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mathematical Science, Schaefer School of Engineering and Science, Stevens Institute of Technology, Hoboken, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangkun","family":"Zeng","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaran","family":"Yan","sequence":"additional","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-205497_ref2","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.eswa.2019.06.002","article-title":"Combine clustering and frequent itemsets mining to enhance biomedical text summarization","volume":"135","author":"Rouane","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"10.3233\/IDA-205497_ref3","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1007\/s00371-018-1511-0","article-title":"Mandible and skull segmentation in cone beam computed tomography using super-voxels and graph clustering","volume":"35","author":"Linares","year":"2019","journal-title":"The Visual Computer"},{"issue":"3","key":"10.3233\/IDA-205497_ref4","doi-asserted-by":"crossref","first-page":"553","DOI":"10.3233\/IDA-150489","article-title":"Fuzzy c-least medians clustering for discovery of web access patterns from web user sessions data","volume":"21","author":"Ansari","year":"2017","journal-title":"Intelligent Data Analysis"},{"issue":"1","key":"10.3233\/IDA-205497_ref5","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s00521-016-2300-1","article-title":"A novel clustering-based image segmentation via density peaks algorithm with mid-level feature","volume":"28","author":"Shi","year":"2017","journal-title":"Neural Computing and Applications"},{"issue":"1","key":"10.3233\/IDA-205497_ref6","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1186\/s13638-018-1179-1","article-title":"Indoor robot localization combining feature clustering with wireless sensor network","volume":"2018","author":"Dong","year":"2018","journal-title":"EURASIP Journal on Wireless Communications and Networking"},{"issue":"4","key":"10.3233\/IDA-205497_ref8","doi-asserted-by":"crossref","first-page":"825","DOI":"10.3233\/IDA-173795","article-title":"Scalable k-means for large-scale clustering","volume":"23","author":"Ming","year":"2019","journal-title":"Intelligent Data Analysis"},{"key":"10.3233\/IDA-205497_ref9","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.knosys.2017.07.010","article-title":"Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy","volume":"133","author":"Yaohui","year":"2017","journal-title":"Knowledge-Based Systems"},{"issue":"S1","key":"10.3233\/IDA-205497_ref10","first-page":"63","article-title":"New density clustering algorithm based on MapReduce","volume":"37","author":"Qiu","year":"2017","journal-title":"Journal of Computer Applications"},{"issue":"1","key":"10.3233\/IDA-205497_ref11","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm, journal of the royal statistical society","volume":"28","author":"Hartigan","year":"1979","journal-title":"Series C (Applied Statistics)"},{"issue":"2","key":"10.3233\/IDA-205497_ref12","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/1467-842X.00071","article-title":"Theory & Methods: Mixture model clustering using the MULTIMIX program","volume":"41","author":"Hunt","year":"1999","journal-title":"Australian & New Zealand Journal of Statistics"},{"issue":"5814","key":"10.3233\/IDA-205497_ref13","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"Frey","year":"2007","journal-title":"Science"},{"issue":"3","key":"10.3233\/IDA-205497_ref14","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1145\/990308.990313","article-title":"On clusterings: Good, bad and spectral","volume":"51","author":"Kannan","year":"2004","journal-title":"Journal of the ACM (JACM)"},{"issue":"34","key":"10.3233\/IDA-205497_ref15","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"},{"issue":"2","key":"10.3233\/IDA-205497_ref17","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/304181.304187","article-title":"OPTICS: Ordering points to identify the clustering structure","volume":"28","author":"Ankerst","year":"1999","journal-title":"ACM Sigmod Record. ACM"},{"key":"10.3233\/IDA-205497_ref18","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.neucom.2016.11.061","article-title":"An adaptive semi-supervised clustering approach via multiple density-based information","volume":"257","author":"Yang","year":"2017","journal-title":"Neurocomputing"},{"key":"10.3233\/IDA-205497_ref19","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.physa.2017.04.119","article-title":"Adaptive density trajectory cluster based on time and space distance","volume":"484","author":"Liu","year":"2017","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"6191","key":"10.3233\/IDA-205497_ref20","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"},{"issue":"12","key":"10.3233\/IDA-205497_ref21","doi-asserted-by":"crossref","first-page":"2125","DOI":"10.1093\/bioinformatics\/bty932","article-title":"Clusterdv: A simple density-based clustering method that is robust, general and automatic","volume":"35","author":"Marques","year":"2018","journal-title":"Bioinformatics"},{"key":"10.3233\/IDA-205497_ref22","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"},{"issue":"7","key":"10.3233\/IDA-205497_ref23","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1016\/j.patrec.2011.02.001","article-title":"APSCAN: A parameter free algorithm for clustering","volume":"32","author":"Chen","year":"2011","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"10.3233\/IDA-205497_ref24","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s40537-019-0236-x","article-title":"Big data clustering with varied density based on MapReduce","volume":"6","author":"Heidari","year":"2019","journal-title":"Journal of Big Data"},{"issue":"7","key":"10.3233\/IDA-205497_ref25","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1016\/j.is.2006.10.006","article-title":"A local-density based spatial clustering algorithm with noise","volume":"32","author":"Duan","year":"2007","journal-title":"Information Systems"},{"key":"10.3233\/IDA-205497_ref26","doi-asserted-by":"crossref","unstructured":"S. Roy and D.K. Bhattacharyya, An approach to find embedded clusters using density based techniques, in: International Conference on Distributed Computing and Internet Technology, Springer, Berlin, Heidelberg, 2005, pp. 523\u2013535.","DOI":"10.1007\/11604655_59"},{"key":"10.3233\/IDA-205497_ref27","doi-asserted-by":"crossref","unstructured":"T.T. Zhang and B. Yuan, Density-based multiscale analysis for clustering in strong noise setting, in: Australasian Joint Conference on Artificial Intelligence, Springer, Cham, 2017, pp. 27\u201338.","DOI":"10.1007\/978-3-319-63004-5_3"},{"key":"10.3233\/IDA-205497_ref28","doi-asserted-by":"crossref","first-page":"25861","DOI":"10.1109\/ACCESS.2018.2836389","article-title":"Density-based multiscale analysis for clustering in strong noise settings with varying densities","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/IDA-205497_ref29","doi-asserted-by":"crossref","unstructured":"B. Borah and D.K. Bhattacharyya, A clustering technique using density difference, in: 2007 International Conference on Signal Processing, Communications and Networking, IEEE, 2007, pp. 585\u2013588.","DOI":"10.1109\/ICSCN.2007.350675"},{"key":"10.3233\/IDA-205497_ref30","doi-asserted-by":"crossref","unstructured":"P. Liu, D. Zhou and N. Wu, VDBSCAN: varied density based spatial clustering of applications with noise, in: 2007 International Conference on Service Systems and Service Management, IEEE, 2007, pp. 1\u20134.","DOI":"10.1109\/ICSSSM.2007.4280175"},{"key":"10.3233\/IDA-205497_ref31","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.patrec.2016.10.014","article-title":"Unsupervised varied density based clustering algorithm using spline","volume":"93","author":"Louhichi","year":"2017","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"10.3233\/IDA-205497_ref32","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s10619-018-7253-1","article-title":"MDCUT 2: A multi-density clustering algorithm with automatic detection of density variation in data with noise","volume":"37","author":"Louhichi","year":"2019","journal-title":"Distributed and Parallel Databases"},{"issue":"3","key":"10.3233\/IDA-205497_ref33","first-page":"329","article-title":"CHAMELEON: A hierarchical clustering algorithm using dynamic modeling","volume":"27","author":"George","year":"1999","journal-title":"IEEE Computer"},{"key":"10.3233\/IDA-205497_ref34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.eswa.2019.04.048","article-title":"A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree","volume":"132","author":"Mishra","year":"2019","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"10.3233\/IDA-205497_ref35","doi-asserted-by":"crossref","first-page":"2939","DOI":"10.1016\/j.eswa.2013.10.025","article-title":"Algorithm to determine \u00ce\u00b5-distance parameter in density based clustering","volume":"41","author":"Jahirabadkar","year":"2014","journal-title":"Expert Systems with Applications"},{"key":"10.3233\/IDA-205497_ref36","doi-asserted-by":"crossref","unstructured":"S.T. Mai, I. Assent and A. Le, Anytime OPTICS: An efficient approach for hierarchical density-based clustering, in: International Conference on Database Systems for Advanced Applications, Springer, Cham, 2016, pp. 164\u2013179.","DOI":"10.1007\/978-3-319-32025-0_11"},{"issue":"14","key":"10.3233\/IDA-205497_ref37","first-page":"249","article-title":"Spatial clustering method for taxi passenger trajectory","volume":"54","author":"Yang","year":"2018","journal-title":"Computer Engineering and Applications"},{"key":"10.3233\/IDA-205497_ref39","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.ins.2016.06.048","article-title":"Development and validation of OPTICS based spatio-temporal clustering technique","volume":"369","author":"Agrawal","year":"2016","journal-title":"Information Sciences"},{"issue":"JAN.1","key":"10.3233\/IDA-205497_ref40","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2015.05.109","article-title":"An efficient and scalable density-based clustering algorithm for datasets with complex structures","volume":"171","author":"Lv","year":"2016","journal-title":"Neurocomputing"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-205497","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:15Z","timestamp":1777454355000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-205497"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":36,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/ida-205497","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,29]]}}}