{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:54:20Z","timestamp":1769831660843,"version":"3.49.0"},"reference-count":14,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,1,25]]},"abstract":"<jats:p>In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).<\/jats:p>","DOI":"10.3233\/jifs-189771","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T13:19:45Z","timestamp":1616159985000},"page":"1051-1059","source":"Crossref","is-referenced-by-count":16,"title":["Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)"],"prefix":"10.1177","volume":"42","author":[{"given":"Saumya","family":"Singh","sequence":"first","affiliation":[{"name":"Instrumentation and Control Engineering Department, Netaji Subhas University of Technology"}]},{"given":"Smriti","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Instrumentation and Control Engineering Department, Netaji Subhas University of Technology"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189771_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2016.01.010"},{"key":"10.3233\/JIFS-189771_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.07.338"},{"key":"10.3233\/JIFS-189771_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2924957"},{"issue":"01","key":"10.3233\/JIFS-189771_ref4","doi-asserted-by":"publisher","first-page":"12","DOI":"10.20894\/IJDMTA.102.006.001.003","article-title":"Grey Wolf Optimizer Based Web usage Data Clustering with Enhanced Fuzzy C Means Algorithm, Integrated Intelligent Research (IIR)","volume":"6","author":"Selvaraju","year":"2017","journal-title":"International Journal of Data Mining Techniques and Applications"},{"key":"10.3233\/JIFS-189771_ref5","doi-asserted-by":"crossref","unstructured":"Dong Z. , Jia H. and Liu M. , An Adaptive Multi-Objective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering, Hindawi Mathematical Problems in Engineering 2018. https:\/\/doi.org\/10.3390\/a11100151","DOI":"10.1155\/2018\/6123874"},{"key":"10.3233\/JIFS-189771_ref9","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.protcy.2012.10.051","article-title":"Improvement of initial clustercenter of c-means using teaching learning-based optimization","volume":"6","author":"Naik","year":"2012","journal-title":"Procedia Technology"},{"issue":"3","key":"10.3233\/JIFS-189771_ref10","first-page":"670","article-title":"Optimizing of Fuzzy C-Means Clustering Algorithm Using GA","volume":"2","author":"Alata","year":"2008","journal-title":"Proceedings of International Journal of Computer, Electrical, Automation, Control and Information Engineering"},{"issue":"3","key":"10.3233\/JIFS-189771_ref12","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1504\/IJAISC.2016.10000220","article-title":"Exploring different kernel functions for kernel-based clustering","volume":"5","author":"Tushir","year":"2016","journal-title":"Proceedings of International Journal on Artificial Intelligence and Soft Computing"},{"key":"10.3233\/JIFS-189771_ref13","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-13731-5_19","article-title":"An Hybrid Approach for Data clustering Using K-Means and Teaching Learning Based Optimization, Emerging ICT for Bridging the Future Vol.2","volume":"338","author":"Mummareddy","year":"2015","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"10.3233\/JIFS-189771_ref14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/978-81-322-1680-3_16","article-title":"Performance Assessment of Kernel-Based Clustering","volume":"246","author":"Srivastava","year":"2014","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"10.3233\/JIFS-189771_ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CIMCA.2005.1631512"},{"issue":"12","key":"10.3233\/JIFS-189771_ref17","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1080\/0305215X.2011.652103","article-title":"Teaching\u2013learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems","volume":"44","author":"Rao","year":"2012","journal-title":"Engineering Optimization"},{"key":"10.3233\/JIFS-189771_ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICSCC.2019.8843624"},{"issue":"2","key":"10.3233\/JIFS-189771_ref20","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1007\/s12204-015-1605-z","article-title":"Improved Kernel Possibilistic Fuzzy Clustering Algorithm Based on Invasive Weed Optimization","volume":"20","author":"Xiao-Qiang","year":"2015","journal-title":"J Shanghai Jiaotong Univ (Sci)"}],"container-title":["Journal of Intelligent &amp; 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