{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T23:49:54Z","timestamp":1770076194448,"version":"3.49.0"},"reference-count":26,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,8,11]]},"abstract":"<jats:p>DKIFCM (Density Based Kernelized Intuitionistic Fuzzy C Means) is the new proposed clustering algorithm that is based on outlier identification, kernel functions, and intuitionist fuzzy approach. DKIFCM is an inspiration from Kernelized Intuitionistic Fuzzy C Means (KIFCM) algorithm and it addresses the performance issue in the presence of outliers. It first identifies outliers based on density of data and then clusters are computed accurately by mapping the data to high dimensional feature space. Performance and effectiveness of various algorithms are evaluated on synthetic 2D data sets such as Diamond data set (D10, D12, and D15), and noisy Dunn data set as well as on high dimension real-world data set such as Fisher-Iris, Wine, and Wisconsin Breast Cancer Data-set. Results of DKIFCM are compared with results of other algorithms such as Fuzzy-C-Means (FCM), Intuitionistic FCM (IFCM), Kernel-Intuitionistic FCM (KIFCM), and density-oriented FCM (DOFCM), and the performance of proposed algorithm is found to be superior even in the presence of outliers and noise. Key advantages of DKIFCM are outlier identification, robustness to noise, and accurate centroid computation.<\/jats:p>","DOI":"10.3233\/jifs-201858","type":"journal-article","created":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T13:39:58Z","timestamp":1616765998000},"page":"2417-2428","source":"Crossref","is-referenced-by-count":8,"title":["An effective fuzzy clustering algorithm with outlier identification feature"],"prefix":"10.1177","volume":"41","author":[{"given":"Anjana","family":"Gosain","sequence":"first","affiliation":[{"name":"USICT, Guru Gobind Singh Indraprastha University, Delhi, India"}]},{"given":"Sonika","family":"Dahiya","sequence":"additional","affiliation":[{"name":"CSE Department, Delhi Technological University, Delhi, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-201858_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining,(Springer","volume":"14","author":"Wu","year":"2008","journal-title":"Knowledge and Information Systems"},{"issue":"3","key":"10.3233\/JIFS-201858_ref3","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)"},{"issue":"4","key":"10.3233\/JIFS-201858_ref5","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1049\/iet-com.2019.0165","article-title":"Enhanced fuzzy C-means clustering based cooperative spectrum sensing combined with multi-objective resource allocation approach for delay-aware CRNs","volume":"14","author":"Sonti","year":"2019","journal-title":"IET Communications"},{"key":"10.3233\/JIFS-201858_ref6","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.eswa.2018.06.055","article-title":"An approach to fault diagnosis with online detection of novel faults using fuzzy clustering tools","volume":"113","author":"Rodr\u00edguez-Ramos","year":"2018","journal-title":"Expert Systems with Applications"},{"issue":"5","key":"10.3233\/JIFS-201858_ref7","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1049\/iet-com.2019.0172","article-title":"Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks","volume":"14","author":"Nancy","year":"2020","journal-title":"IET Communications"},{"issue":"15","key":"10.3233\/JIFS-201858_ref8","doi-asserted-by":"crossref","first-page":"4971","DOI":"10.1007\/s00500-018-3191-0","article-title":"An interval type 2 hesitant fuzzy MCDM approach and a fuzzy c means clustering for retailer clustering","volume":"22","author":"Oner","year":"2018","journal-title":"Soft Computing"},{"issue":"3","key":"10.3233\/JIFS-201858_ref9","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1049\/iet-ipr.2018.5949","article-title":"Image segmentation algorithm based on neutrosophic fuzzy clustering with non-local information","volume":"14","author":"Wen","year":"2019","journal-title":"IET Image Processing"},{"key":"10.3233\/JIFS-201858_ref10","doi-asserted-by":"crossref","first-page":"4500","DOI":"10.1109\/ACCESS.2019.2963444","article-title":"An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-201858_ref14","first-page":"62","article-title":"Scalable and sustainable wireless sensor networks for agricultural application of Internet of things using fuzzy c-means algorithm","volume":"22","author":"Rajput","year":"2019","journal-title":"Sustainable Computing: Informatics and Systems"},{"issue":"3","key":"10.3233\/JIFS-201858_ref17","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","article-title":"Fuzzy sets","volume":"8","author":"Zadeh","year":"1965","journal-title":"Information and Control"},{"issue":"2-3","key":"10.3233\/JIFS-201858_ref19","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Computers & Geosciences"},{"issue":"2","key":"10.3233\/JIFS-201858_ref20","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/91.227387","article-title":"A possibilistic approach to clustering","volume":"1","author":"Krishnapuram","year":"1993","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"11","key":"10.3233\/JIFS-201858_ref21","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/0167-8655(91)90002-4","article-title":"Characterization and detection of noise in clustering","volume":"12","author":"Dave","year":"1991","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/JIFS-201858_ref22","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1109\/ICSMC.1998.728197","article-title":"The credibilistic fuzzy c means clustering algorithm, In","volume":"2","author":"Chintalapudi","year":"1998","journal-title":"SMC\u201998 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No. 98CH36218), IEEE"},{"issue":"4","key":"10.3233\/JIFS-201858_ref23","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1109\/TFUZZ.2004.840099","article-title":"A possibilistic fuzzy c-means clustering algorithm","volume":"13","author":"Pal","year":"2005","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"2","key":"10.3233\/JIFS-201858_ref24","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1504\/IJICA.2011.039591","article-title":"A density oriented fuzzy c-means clustering algorithm for recognising original cluster shapes from noisy data","volume":"3","author":"Kaur","year":"2011","journal-title":"International Journal of Innovative Computing and Applications"},{"issue":"2","key":"10.3233\/JIFS-201858_ref26","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1016\/j.asoc.2010.05.005","article-title":"A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images","volume":"11","author":"Chaira","year":"2011","journal-title":"Applied Soft Computing"},{"issue":"4","key":"10.3233\/JIFS-201858_ref27","doi-asserted-by":"crossref","first-page":"580","DOI":"10.3969\/j.issn.1004-4132.2010.04.009","article-title":"Intuitionistic fuzzy c-means clustering algorithms","volume":"21","author":"Xu","year":"2010","journal-title":"Journal of Systems Engineering and Electronics"},{"key":"10.3233\/JIFS-201858_ref29","doi-asserted-by":"crossref","unstructured":"Kaur P. , Soni A.K. and Gosain A. , Robust Intuitionistic Fuzzy C-means clustering for linearly and nonlinearly separable data, International Conference on Image Information Processing, IEEE, (2011), 1\u20136.","DOI":"10.1109\/ICIIP.2011.6108908"},{"issue":"9","key":"10.3233\/JIFS-201858_ref30","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1080\/08839514.2010.514194","article-title":"\u2018IMIOL: a system for indexing images by their semantic content based on possibilistic fuzzy clustering and adaptive resonance theory neural networks learning","volume":"24","author":"Romdhane","year":"2010","journal-title":"Applied Artificial Intelligence"},{"key":"10.3233\/JIFS-201858_ref31","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.procs.2016.03.014","article-title":"Performance analysis of various fuzzy clustering algorithms: a review","volume":"79","author":"Gosain","year":"2016","journal-title":"Procedia Computer Science"},{"issue":"2","key":"10.3233\/JIFS-201858_ref32","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1016\/j.asoc.2009.08.020","article-title":"A new Kernelized hybrid c-mean clustering model with optimized parameters","volume":"10","author":"Tushir","year":"2010","journal-title":"Applied Soft Computing"},{"issue":"34","key":"10.3233\/JIFS-201858_ref33","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":"In Kdd"},{"issue":"3","key":"10.3233\/JIFS-201858_ref34","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1007\/s11063-020-10345-1","article-title":"A New Robust Fuzzy Clustering Approach: DBKIFCM","volume":"52","author":"Gosain","year":"2020","journal-title":"Neural Processing Letters"},{"key":"10.3233\/JIFS-201858_ref37","doi-asserted-by":"crossref","unstructured":"Dahiya S. , Nanda H. , Artwani J. and Varshney J. , Using Clustering techniques and Classification Mechanisms for Fault Diagnosis, International Journal 9(2) (2020).","DOI":"10.30534\/ijatcse\/2020\/188922020"},{"issue":"2","key":"10.3233\/JIFS-201858_ref38","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.engappai.2012.07.002","article-title":"Robust kernelized approach to clustering by incorporating new distance measure","volume":"26","author":"Kaur","year":"2013","journal-title":"Engineering Applications of Artificial Intelligence"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-201858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T01:16:49Z","timestamp":1769995009000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-201858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,11]]},"references-count":26,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-201858","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,11]]}}}