{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T16:32:31Z","timestamp":1789835551298,"version":"4.0.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cluster Comput"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s10586-022-03549-8","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T19:03:36Z","timestamp":1642705416000},"page":"1441-1457","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multi objective-based incremental clustering by fast search technique for dynamically creating and updating clusters in large data"],"prefix":"10.1007","volume":"25","author":[{"given":"Sivadi","family":"Balakrishna","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"3549_CR1","doi-asserted-by":"publisher","first-page":"104824","DOI":"10.1016\/j.knosys.2019.06.032","volume":"187","author":"Y Chen","year":"2020","unstructured":"Chen, Y., Hu, X., Fan, W., Shen, L., Zhang, Z., Liu, X., Du, J., Li, H., Chen, Y., Li, H.: Fast density peak clustering for large scale data based on kNN. Knowl. Based Syst. 187, 104824 (2020)","journal-title":"Knowl. Based Syst."},{"key":"3549_CR2","doi-asserted-by":"publisher","unstructured":"Abu Khurma, R., Aljarah, I.: A review of multiobjective evolutionary algorithms for data clustering problems. In: Evolutionary Data Clustering: Algorithms and Applications, pp. 177\u2013199 (2021). https:\/\/doi.org\/10.1007\/978-981-33-4191-3_8","DOI":"10.1007\/978-981-33-4191-3_8"},{"issue":"11","key":"3549_CR3","doi-asserted-by":"publisher","first-page":"2348","DOI":"10.1109\/TCAD.2018.2858384","volume":"37","author":"Z Zhao","year":"2018","unstructured":"Zhao, Z., Barijough, K.M., Gerstlauer, A.: DeepThings: distributed adaptive deep learning inference on resource-constrained IoT edge clusters. IEEE Trans Comput. Aided Des. Integr. Circuits Syst. 37(11), 2348\u20132359 (2018)","journal-title":"IEEE Trans Comput. Aided Des. Integr. Circuits Syst."},{"issue":"1","key":"3549_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/TETCI.2018.2876568","volume":"4","author":"W Wang","year":"2020","unstructured":"Wang, W., Zhang, M.: Tensor deep learning model for heterogeneous data fusion in Internet of Things. IEEE Trans. Emerg. Top. Comput. Intell. 4(1), 32\u201341 (2020)","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"3549_CR5","doi-asserted-by":"publisher","first-page":"81691","DOI":"10.1109\/ACCESS.2019.2923694","volume":"7","author":"Y Weng","year":"2019","unstructured":"Weng, Y., Zhang, N., Yang, X.: Improved density peak clustering based on information entropy for ancient character images. IEEE Access 7, 81691\u201381700 (2019)","journal-title":"IEEE Access"},{"issue":"19","key":"3549_CR6","doi-asserted-by":"publisher","first-page":"6466","DOI":"10.3390\/s21196466","volume":"21","author":"Z Jiang","year":"2021","unstructured":"Jiang, Z., Lin, R., Yang, F.: An incremental clustering algorithm with pattern drift detection for IoT-enabled smart grid system. Sensors 21(19), 6466 (2021)","journal-title":"Sensors"},{"key":"3549_CR7","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-189782","author":"SP Ajith Kumar","year":"2021","unstructured":"Ajith Kumar, S.P., Banyal, S., Bharadwaj, K.K., Thakur, H.K., Sharma, D.K.: Distributed probability density based multi-objective routing for Opp-IoT networks enabled by machine learning. J. Intell. Fuzzy Syst. (2021). https:\/\/doi.org\/10.3233\/JIFS-189782","journal-title":"J. Intell. Fuzzy Syst."},{"key":"3549_CR8","doi-asserted-by":"publisher","first-page":"41637","DOI":"10.1109\/ACCESS.2020.2976699","volume":"8","author":"Y Yin","year":"2020","unstructured":"Yin, Y., Long, L., Deng, X.: Dynamic data mining of sensor data. IEEE Access 8, 41637\u201341648 (2020)","journal-title":"IEEE Access"},{"key":"3549_CR9","doi-asserted-by":"crossref","unstructured":"Balakrishna, S., Thirumaran, M., Padmanaban, R., Solanki, V.K.: An efficient incremental clustering-based improved K-medoids for IoT multivariate data cluster analysis. Peer-to-Peer Netw. Appl. 13(4), 1152\u20131175 (2020). ISSN 1936\u20136442","DOI":"10.1007\/s12083-019-00852-x"},{"key":"3549_CR10","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.ins.2021.04.076","volume":"571","author":"D-T Dinh","year":"2021","unstructured":"Dinh, D.-T., Huynh, V.-N., Sriboonchitta, S.: Clustering mixed numerical and categorical data with missing values. Inf. Sci. 571, 418\u2013442 (2021)","journal-title":"Inf. Sci."},{"issue":"2","key":"3549_CR11","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","volume":"54","author":"A Zubaro\u011flu","year":"2021","unstructured":"Zubaro\u011flu, A., Atalay, V.: Data stream clustering: a review. Artif. Intell. Rev. 54(2), 1201\u20131236 (2021)","journal-title":"Artif. Intell. Rev."},{"issue":"2","key":"3549_CR12","first-page":"9595","volume":"58","author":"APS Kushwah","year":"2021","unstructured":"Kushwah, A.P.S., Jaloree, S., Thakur, R.S.: A comparative review of incremental clustering methods for large dataset. Psychol. Educ. J. 58(2), 9595\u20139607 (2021)","journal-title":"Psychol. Educ. J."},{"issue":"6191","key":"3549_CR13","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492\u20131496 (2014)","journal-title":"Science"},{"issue":"11","key":"3549_CR14","doi-asserted-by":"publisher","first-page":"2731","DOI":"10.1109\/TKDE.2014.2310215","volume":"26","author":"L Sun","year":"2014","unstructured":"Sun, L., Guo, C.: Incremental affinity propagation clustering based on message passing. IEEE Trans. Knowl. Data Eng. 26(11), 2731\u20132744 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"3549_CR15","unstructured":"Zhang, X., Furtlehner, C., Sebag, M.: Frugal and online affinity propagation. In: Proceedings of Conference Francophone sur l\u2019Apprentissage (CAP), 2008. https:\/\/hal.inria.fr\/inria-00287381"},{"key":"3549_CR16","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Nagwani, N.K.: Analysis and study of incremental K-means clustering algorithm. In: High Performance Architecture and Grid Computing, pp. 338\u2013341. Springer, Berlin (2011)","DOI":"10.1007\/978-3-642-22577-2_46"},{"issue":"7","key":"3549_CR17","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/TNN.2009.2019722","volume":"20","author":"GF Tzortzis","year":"2009","unstructured":"Tzortzis, G.F., Likas, A.C.: The global kernel K-means algorithm for clustering in feature space. IEEE Trans. Neural Netw. 20(7), 1181\u20131194 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"3","key":"3549_CR18","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/TNNLS.2018.2851979","volume":"30","author":"L Zhao","year":"2019","unstructured":"Zhao, L., Chen, Z., Yang, Y., Zou, L., Jane Wang, Z.: ICFS clustering multiple representatives for large data. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 728\u2013738 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"6","key":"3549_CR19","doi-asserted-by":"publisher","first-page":"1557","DOI":"10.1109\/TFUZZ.2014.2298244","volume":"22","author":"Y Wang","year":"2014","unstructured":"Wang, Y., Chen, L., Mei, J.-P.: Incremental fuzzy clustering with multiple medoids for large data. IEEE Trans. Fuzzy Syst. 22(6), 1557\u20131568 (2014)","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"3549_CR20","doi-asserted-by":"publisher","first-page":"1193","DOI":"10.1109\/TII.2017.2684807","volume":"13","author":"Q Zhang","year":"2017","unstructured":"Zhang, Q., Zhu, C., Yang, L.T., Chen, Z., Zhao, L., Li, P.: An incremental CFS algorithm for clustering large data in the industrial Internet of Things. IEEE Trans. Ind. Inform. 13(3), 1193\u20131201 (2017)","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"6","key":"3549_CR21","first-page":"1","volume":"28","author":"Y Tian","year":"2019","unstructured":"Tian, Y., Yang, S., Zhang, X.: An evolutionary multi-objective optimization based fuzzy method for overlapping community detection. IEEE Trans. Fuzzy Syst. 28(6), 1\u201315 (2019)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"3549_CR22","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.eswa.2018.06.047","volume":"113","author":"Z Zareizadeh","year":"2018","unstructured":"Zareizadeh, Z., Helfroush, M.S., Rahideh, A., Kazemi, K.: A robust gene clustering algorithm based on the clonal selection in a multi-objective optimization framework. Expert Syst. Appl. 113, 301\u2013314 (2018)","journal-title":"Expert Syst. Appl."},{"key":"3549_CR23","doi-asserted-by":"publisher","first-page":"90863","DOI":"10.1109\/ACCESS.2019.2927239","volume":"7","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Liu, Y., Wu, F., Fan, W., Tang, J., Liu, H.: Multi-dimensional joint prediction model for IoT sensor data search. IEEE Access 7, 90863\u201390873 (2019)","journal-title":"IEEE Access"},{"key":"3549_CR24","doi-asserted-by":"crossref","unstructured":"Chander, S., Vijaya, P., Dhyani, P.: MO-ADDOFL: Multi-objective-based adaptive dynamic directive operative fractional lion algorithm for data clustering. In: 2018 Majan International Conference (MIC), pp. 1\u20136. IEEE, Muscat (2018)","DOI":"10.1109\/MINTC.2018.8363149"},{"key":"3549_CR25","doi-asserted-by":"crossref","unstructured":"A.A. Naeini, S. Homayouni, and M. Saadatseresht (2014) Improving the dynamic clustering of hyperspectral data based on the integration of swarm optimization and decision analysis, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2161\u20132173.","DOI":"10.1109\/JSTARS.2014.2307579"},{"issue":"2","key":"3549_CR26","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1109\/TC.2017.2742513","volume":"67","author":"RR Manumachu","year":"2018","unstructured":"Manumachu, R.R., Lastovetsky, A.: Bi-objective optimization of data-parallel applications on homogeneous multicore clusters for performance and energy. IEEE Trans. Comput. 67(2), 160\u2013177 (2018)","journal-title":"IEEE Trans. Comput."},{"key":"3549_CR27","doi-asserted-by":"publisher","unstructured":"Habib, M., Aljarah, I., Faris, H., Mirjalili, S.: Multi-objective particle swarm optimization for botnet detection in Internet of Things. In: Evolutionary Machine Learning Techniques, pp. 203\u2013229. Springer (2020). https:\/\/doi.org\/10.1007\/978-981-32-9990-0_10","DOI":"10.1007\/978-981-32-9990-0_10"},{"issue":"12","key":"3549_CR28","first-page":"1","volume":"12","author":"C Gong","year":"2017","unstructured":"Gong, C., Chen, H., He, W., Zhang, Z.: Improved multi-objective clustering algorithm using particle swarm optimization. PLoS ONE 12(12), 1\u201319 (2017)","journal-title":"PLoS ONE"},{"issue":"1","key":"3549_CR29","first-page":"1","volume":"2018","author":"Z Dong","year":"2018","unstructured":"Dong, Z., Jia, H., Liu, M.: An adaptive multiobjective genetic algorithm with fuzzy-means for automatic data clustering. Math. Probl. Eng. 2018(1), 1\u201313 (2018)","journal-title":"Math. Probl. Eng."},{"key":"3549_CR30","doi-asserted-by":"crossref","unstructured":"Chander, S., Vijaya, P., Dhyani, P.: Fgv MO-ADDOFL: multi-objective-based adaptive dynamic directive operative fractional lion algorithm for data clustering. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), Dubai, pp. 533\u2013539, 2017","DOI":"10.1109\/ICTUS.2017.8286066"},{"key":"3549_CR31","first-page":"1","volume":"2016","author":"F Bu","year":"2016","unstructured":"Bu, F., Chen, Z., Li, P., Tang, T., Zhang, Y.: A high-order CFS algorithm for clustering big data. Mob. Inf. Syst. 2016, 1\u20138 (2016)","journal-title":"Mob. Inf. Syst."},{"key":"3549_CR32","first-page":"1","volume":"2017","author":"S Liu","year":"2017","unstructured":"Liu, S., Zhou, B., Huang, D., Shen, L.: Clustering mixed data by fast search and find of density peaks. Math. Probl. Eng. 2017, 1\u20137 (2017)","journal-title":"Math. Probl. Eng."},{"key":"3549_CR33","doi-asserted-by":"publisher","first-page":"74612","DOI":"10.1109\/ACCESS.2019.2918772","volume":"7","author":"L Zhuo","year":"2019","unstructured":"Zhuo, L., Li, K., Liao, B., Li, H., Wei, X., Li, K.: HCFS: a density peak based clustering algorithm employing a hierarchical strategy. IEEE Access 7, 74612\u201374624 (2019)","journal-title":"IEEE Access"},{"key":"3549_CR34","unstructured":"https:\/\/data.world\/cityofchicago\/beach-weather-stations-automated-sensors\/workspace\/file?filename=beach-weather-stations-automated-sensors-1.csv"},{"key":"3549_CR35","doi-asserted-by":"publisher","unstructured":"Alswailim, M.A., Hassanein, H.S., Zulkernine, M.: CRAWDAD dataset queensu\/crowd_temperature (v. 2015 11 20): derived from roma\/taxi (v. 2014 07 17). https:\/\/crawdad.org\/queensu\/crowd_temperature\/20151120, https:\/\/doi.org\/10.15783\/C7CG65. Accessed Nov 2015","DOI":"10.15783\/C7CG65"},{"key":"3549_CR36","unstructured":"https:\/\/www.kaggle.com\/julianjose\/minute-weather"},{"key":"3549_CR37","unstructured":"http:\/\/wiki.knoesis.org\/index.php\/LinkedSensorData"}],"container-title":["Cluster Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03549-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10586-022-03549-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10586-022-03549-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,19]],"date-time":"2022-03-19T20:24:48Z","timestamp":1647721488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10586-022-03549-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["3549"],"URL":"https:\/\/doi.org\/10.1007\/s10586-022-03549-8","relation":{},"ISSN":["1386-7857","1573-7543"],"issn-type":[{"value":"1386-7857","type":"print"},{"value":"1573-7543","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,20]]},"assertion":[{"value":"31 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}