{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:59:37Z","timestamp":1743004777466,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030755287"},{"type":"electronic","value":"9783030755294"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-75529-4_10","type":"book-chapter","created":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T19:02:43Z","timestamp":1621969363000},"page":"118-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pythagorean Fuzzy c-means Clustering Algorithm"],"prefix":"10.1007","author":[{"given":"Souvik","family":"Gayen","sequence":"first","affiliation":[]},{"given":"Animesh","family":"Biswas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,26]]},"reference":[{"key":"10_CR1","volume-title":"Cluster Analysis for Applications","author":"MR Anderberg","year":"1972","unstructured":"Anderberg, M.R.: Cluster Analysis for Applications, 1st edn. Academic Press, New York (1972)","edition":"1"},{"issue":"2","key":"10_CR2","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/0377-2217(96)00038-0","volume":"93","author":"P Mangiameli","year":"1996","unstructured":"Mangiameli, P., Chen, S.K., West, D.: A comparison of SOM neural network and hierarchical clustering methods. Eur. J. Oper. Res. 93(2), 402\u2013417 (1996)","journal-title":"Eur. J. Oper. Res."},{"key":"10_CR3","volume-title":"Cluster Analysis","author":"BS Everitt","year":"2001","unstructured":"Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis, 5th edn. Oxford University Press, New York (2001)","edition":"5"},{"issue":"3","key":"10_CR4","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. 31(3), 264\u2013323 (1999)","journal-title":"ACM Comput. Surv."},{"issue":"3","key":"10_CR5","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/j.ejor.2005.10.007","volume":"173","author":"G Beliakov","year":"2006","unstructured":"Beliakov, G., King, M.: Density based fuzzy C-means clustering of non-convex patterns. Eur. J. Oper. Res. 173(3), 717\u2013728 (2006)","journal-title":"Eur. J. Oper. Res."},{"key":"10_CR6","volume-title":"Fuzzy Sets and Fuzzy Logic: Theory and Applications","author":"GJ Klir","year":"1994","unstructured":"Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall, New York (1994)"},{"key":"10_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"JC Bezdek","year":"1981","unstructured":"Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)"},{"key":"10_CR8","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy C-means clustering algorithm. Comput. Geosci. 10, 191\u2013203 (1984)","journal-title":"Comput. Geosci."},{"key":"10_CR9","first-page":"768","volume":"21","author":"E Forgey","year":"1965","unstructured":"Forgey, E.: Cluster analysis of multivariate data: efficiency vs Interpretability of Classification. Biometrics 21, 768\u2013769 (1965)","journal-title":"Biometrics"},{"issue":"1","key":"10_CR10","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0165-0114(86)80034-3","volume":"20","author":"KT Atanassov","year":"1986","unstructured":"Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87\u201396 (1986)","journal-title":"Fuzzy Sets Syst."},{"key":"10_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-1870-3","volume-title":"Intuitionistic Fuzzy Sets: Theory and Applications","author":"KT Atanassov","year":"1999","unstructured":"Atanassov, K.T.: Intuitionistic Fuzzy Sets: Theory and Applications. Physica-Verlag, Heidelberg (1999)"},{"issue":"4","key":"10_CR12","doi-asserted-by":"publisher","first-page":"580","DOI":"10.3969\/j.issn.1004-4132.2010.04.009","volume":"21","author":"Z Xu","year":"2010","unstructured":"Xu, Z., Wu, J.: Intuitionistic fuzzy c-mean clustering algorithms . J. Syst. Eng. Electron. 21(4), 580\u2013590 (2010)","journal-title":"J. Syst. Eng. Electron."},{"issue":"3","key":"10_CR13","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/S0165-0114(98)00244-9","volume":"114","author":"E Szmidt","year":"2000","unstructured":"Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets Syst. 114(3), 505\u2013518 (2000)","journal-title":"Fuzzy Sets Syst."},{"issue":"2","key":"10_CR14","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1016\/j.asoc.2010.05.005","volume":"11","author":"T Chaira","year":"2011","unstructured":"Chaira, T.: A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Appl. Soft Comput. 11(2), 1711\u20131717 (2011)","journal-title":"Appl. Soft Comput."},{"issue":"4","key":"10_CR15","first-page":"1","volume":"27","author":"SA Kumar","year":"2017","unstructured":"Kumar, S.A., Harish, B.S.: A Modified intuitionistic fuzzy clustering algorithm for medical image segmentation. J. Intell. Syst. 27(4), 1\u201315 (2017)","journal-title":"J. Intell. Syst."},{"key":"10_CR16","first-page":"1","volume":"94","author":"C Wu","year":"2020","unstructured":"Wu, C., Zhang, X.: Total Bregman divergence-based fuzzy local information C-means clustering for robust image segmentation. Appl. Soft Comput. 94, 1\u201331 (2020)","journal-title":"Appl. Soft Comput."},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.neucom.2019.01.042","volume":"335","author":"S Zeng","year":"2019","unstructured":"Zeng, S., Wang, Z., Huang, R., Chen, L., Feng, D.: A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes. Neurocomputing 335, 59\u201371 (2019)","journal-title":"Neurocomputing"},{"key":"10_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.patrec.2019.02.017","volume":"122","author":"H Verman","year":"2019","unstructured":"Verman, H., Gupta, A., Kumar, D.: A modified intuitionistic fuzzy c-mean algorithm incorporating hesitation degree. Pattern Recognit. Lett. 122, 45\u201352 (2019)","journal-title":"Pattern Recognit. Lett."},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"1176","DOI":"10.1016\/j.asoc.2018.04.014","volume":"71","author":"QMD Lohani","year":"2018","unstructured":"Lohani, Q.M.D., Solanki, R., Muhuri, P.K.: A Convergence theorem and an experimental study of intuitionistic fuzzy C-mean algorithm over machine learning dataset. Appl. Soft Comput. 71, 1176\u20131188 (2018)","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10_CR20","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/S0165-0114(98)00279-6","volume":"114","author":"H Bustince","year":"2000","unstructured":"Bustince, H., Kacprzyk, J., Mohedano, V.: Intuitionistic fuzzy generators: application to intuitionistic fuzzy complementation. Fuzzy Sets Syst. 114(3), 485\u2013504 (2000)","journal-title":"Fuzzy Sets Syst."},{"key":"10_CR21","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1002\/int.21584","volume":"28","author":"RR Yager","year":"2013","unstructured":"Yager, R.R., Abbasov, A.M.: Pythagorean membership grades, complex numbers, and decision making. Int. J. Intell. Syst. 28, 436\u2013452 (2013)","journal-title":"Int. J. Intell. Syst."},{"issue":"6","key":"10_CR22","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/0167-8655(96)00026-8","volume":"17","author":"RN Dave","year":"1996","unstructured":"Dave, R.N.: Validating fuzzy partition obtained through c-shells clustering. Pattern Recognit. Lett. 17(6), 613\u2013623 (1996)","journal-title":"Pattern Recognit. Lett."},{"issue":"8","key":"10_CR23","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/34.85677","volume":"13","author":"XL Xie","year":"1991","unstructured":"Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 13(8), 841\u2013847 (1991)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"3","key":"10_CR24","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/91.413225","volume":"3","author":"NR Pal","year":"1995","unstructured":"Pal, N.R., Bezdek, J.C.: On cluster validity for the fuzzy c-means model. IEEE Trans. Fuzzy Syst. 3(3), 370\u2013379 (1995)","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10_CR25","first-page":"338","volume":"33","author":"D Li","year":"2017","unstructured":"Li, D., Zeng, W.: Distance measure of pythagorean fuzzy sets. Int. J. Intell. Syst. 33, 338\u2013361 (2017)","journal-title":"Int. J. Intell. Syst."},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Ito, K., Kunisch, K.: Lagrange multiplier approach to variational problems and applications. Advances in Design and Control, Philadelphia (2008)","DOI":"10.1137\/1.9780898718614"},{"issue":"3","key":"10_CR27","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1080\/03081078208547446","volume":"8","author":"M Higashi","year":"1982","unstructured":"Higashi, M., Klir, G.J.: On measures of fuzzyness and fuzzy complements. Int. J. Gen. Syst. 8(3), 169\u2013180 (1982)","journal-title":"Int. J. Gen. Syst."},{"key":"10_CR28","unstructured":"Wu, K.-L.: An analysis of robustness of partition coefficient index. In: 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), Hong Kong, pp. 372\u2013376 (2008)"}],"container-title":["Communications in Computer and Information Science","Computational Intelligence in Communications and Business Analytics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75529-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,25]],"date-time":"2021-05-25T19:05:09Z","timestamp":1621969509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75529-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030755287","9783030755294"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75529-4_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"26 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICBA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Communications and Business Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiniketan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicba2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.cicba.in","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"84","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.44","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.54","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}