{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T22:47:25Z","timestamp":1742942845179,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030616557"},{"type":"electronic","value":"9783030616564"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61656-4_10","type":"book-chapter","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:03:54Z","timestamp":1604505834000},"page":"150-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparison Analysis of Clustering Quality Criteria Using Inductive Methods of Objective Clustering"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6797-1467","authenticated-orcid":false,"given":"Sergii","family":"Babichev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7574-4133","authenticated-orcid":false,"given":"Aleksander","family":"Spivakovskiy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5724-0759","authenticated-orcid":false,"given":"Ji\u0159\u00ed","family":"\u0160kvor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,5]]},"reference":[{"key":"10_CR1","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1007\/978-3-030-26474-1_37","volume-title":"Lecture Notes in Computational Intelligence and Decision Making","author":"S Babichev","year":"2020","unstructured":"Babichev, S., Durnyak, B., Pikh, I., Senkivskyy, V.: An evaluation of the objective clustering inductive technology effectiveness implemented using density-based and agglomerative hierarchical clustering algorithms. In: Lytvynenko, V., Babichev, S., W\u00f3jcik, W., Vynokurova, O., Vyshemyrskaya, S., Radetskaya, S. (eds.) ISDMCI 2019. AISC, vol. 1020, pp. 532\u2013553. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-26474-1_37"},{"key":"10_CR2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-3-319-58274-0_29","volume-title":"Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation","author":"S Babichev","year":"2017","unstructured":"Babichev, S., Lytvynenko, V., Korobchynskyi, M., Taiff, M.A.: Objective clustering inductive technology of gene expression sequences features. In: Kozielski, S., Mrozek, D., Kasprowski, P., Ma\u0142ysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2017. CCIS, vol. 716, pp. 359\u2013372. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58274-0_29"},{"key":"10_CR3","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-3-319-70581-1_2","volume-title":"Advances in Intelligent Systems and Computing II","author":"S Babichev","year":"2018","unstructured":"Babichev, S., Lytvynenko, V., Skvor, J., Fiser, J.: Model of the objective clustering inductive technology of gene expression profiles based on SOTA and DBSCAN clustering algorithms. In: Shakhovska, N., Stepashko, V. (eds.) CSIT 2017. AISC, vol. 689, pp. 21\u201339. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-70581-1_2"},{"key":"10_CR4","doi-asserted-by":"publisher","first-page":"54","DOI":"10.15588\/1607-3274-2016-4-7","volume":"4","author":"S Babichev","year":"2016","unstructured":"Babichev, S., Taif, M., Lytvynenko, V.: Estimation of the inductive model of objects clustering stability based on the k-means algorithm for different levels of data noise. Radio Electron. Comput. Sci. Control 4, 54\u201360 (2016). https:\/\/doi.org\/10.15588\/1607-3274-2016-4-7","journal-title":"Radio Electron. Comput. Sci. Control"},{"key":"10_CR5","doi-asserted-by":"publisher","unstructured":"Babichev, S., Taif, M., Lytvynenko, V., Osypenko, V.: Criterial analysis of gene expression sequences to create the objective clustering inductive technology. In: 2017 IEEE 37th International Conference on Electronics and Nanotechnology, pp. 244\u2013248 (2017). https:\/\/doi.org\/10.1109\/ELNANO.2017.7939756","DOI":"10.1109\/ELNANO.2017.7939756"},{"key":"10_CR6","first-page":"1","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3, 1\u201327 (1974)","journal-title":"Commun. Stat."},{"key":"10_CR7","unstructured":"Desgraupes, B.: Compute clustering validation indices (2018). https:\/\/cran.r-project.org\/web\/packages\/clusterCrit"},{"key":"10_CR8","doi-asserted-by":"publisher","unstructured":"Elm, J., Kube\u010dka, J., Besel, V., et al.: Modeling the formation and growth of atmospheric molecular clusters: a review. J. Aerosol Sci. 149, Article no. 105621 (2020). https:\/\/doi.org\/10.1016\/j.jaerosci.2020.105621","DOI":"10.1016\/j.jaerosci.2020.105621"},{"key":"10_CR9","series-title":"Smart Innovation, Systems and Technologies","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-981-15-5093-5_17","volume-title":"Progresses in Artificial Intelligence and Neural Systems","author":"AM Esposito","year":"2021","unstructured":"Esposito, A.M., Alaia, G., Giudicepietro, F., Pappalardo, L., D\u2019Antonio, M.: Unsupervised geochemical analysis of the eruptive products of Ischia, Vesuvius and Campi Flegrei. In: Esposito, A., Faundez-Zanuy, M., Morabito, F.C., Pasero, E. (eds.) Progresses in Artificial Intelligence and Neural Systems. SIST, vol. 184, pp. 175\u2013184. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-5093-5_17"},{"key":"10_CR10","unstructured":"Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial datasets with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231 (1996)"},{"issue":"12","key":"10_CR11","doi-asserted-by":"publisher","first-page":"4743","DOI":"10.1007\/s10489-018-1238-7","volume":"48","author":"P Fr\u00e4nti","year":"2018","unstructured":"Fr\u00e4nti, P., Sieranoja, S.: K-means properties on six clustering benchmark datasets. Appl. Intell. 48(12), 4743\u20134759 (2018). https:\/\/doi.org\/10.1007\/s10489-018-1238-7","journal-title":"Appl. Intell."},{"key":"10_CR12","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.future.2020.07.023","volume":"113","author":"X Guo","year":"2020","unstructured":"Guo, X., Lin, H., Wu, Y., Peng, M.: A new data clustering strategy for enhancing mutual privacy in healthcare IoT systems. Future Gener. Comput. Syst. 113, 407\u2013417 (2020). https:\/\/doi.org\/10.1016\/j.future.2020.07.023","journal-title":"Future Gener. Comput. Syst."},{"key":"10_CR13","unstructured":"Hahsler, M., Piekenbrock, M., Arya, S., Mount, D.: Density based clustering of applications with noise (DBSCAN) and related algorithms (2019). https:\/\/github.com\/mhahsler\/dbscan"},{"issue":"10","key":"10_CR14","first-page":"494","volume":"21","author":"J Harrington","year":"1965","unstructured":"Harrington, J.: The desirability function. Ind. Qual. Control 21(10), 494\u2013498 (1965). http:\/\/asq.org\/qic\/display-item\/?item=4860","journal-title":"Ind. Qual. Control"},{"key":"10_CR15","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-030-39216-1_33","volume-title":"Advances in Intelligent Systems, Computer Science and Digital Economics","author":"Z Hu","year":"2020","unstructured":"Hu, Z., Tyshchenko, O.K.: An approach to online fuzzy clustering based on the Mahalanobis distance measure. In: Hu, Z., Petoukhov, S., He, M. (eds.) CSDEIS 2019. AISC, vol. 1127, pp. 364\u2013374. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-39216-1_33"},{"issue":"3","key":"10_CR16","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/10618600.1996.10474713","volume":"5","author":"R Ihaka","year":"1996","unstructured":"Ihaka, R., Gentleman, R.: R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5(3), 299\u2013314 (1996)","journal-title":"J. Comput. Graph. Stat."},{"key":"10_CR17","first-page":"6","volume":"5","author":"A Ivakhnenko","year":"1987","unstructured":"Ivakhnenko, A.: Objective clustering based on the theory of self-organization models. Automatics 5, 6\u201315 (1987)","journal-title":"Automatics"},{"key":"10_CR18","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/978-3-030-29035-1_43","volume-title":"Advances in Intelligent Networking and Collaborative Systems","author":"I Izonin","year":"2020","unstructured":"Izonin, I., Kryvinska, N., Vitynskyi, P., Tkachenko, R., Zub, K.: GRNN approach towards missing data recovery between IoT systems. In: Barolli, L., Nishino, H., Miwa, H. (eds.) INCoS 2019. AISC, vol. 1035, pp. 445\u2013453. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-29035-1_43"},{"key":"10_CR19","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-319-70581-1_14","volume-title":"Advances in Intelligent Systems and Computing II","author":"O Kanishcheva","year":"2018","unstructured":"Kanishcheva, O., Vysotska, V., Chyrun, L., Gozhyj, A.: Method of integration and content management of the information resources network. In: Shakhovska, N., Stepashko, V. (eds.) CSIT 2017. AISC, vol. 689, pp. 204\u2013216. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-70581-1_14"},{"key":"10_CR20","doi-asserted-by":"publisher","unstructured":"Liu, Z., Barahona, M.: Graph-based data clustering via multiscale community detection. Appl. Netw. Sci. 5(1), Article no. 3 (2020). https:\/\/doi.org\/10.1007\/s41109-019-0248-7","DOI":"10.1007\/s41109-019-0248-7"},{"key":"10_CR21","first-page":"380","volume-title":"Inductive Learning Algorithms for Complex Systems Modeling","author":"H Madala","year":"1994","unstructured":"Madala, H., Ivakhnenko, A.: Inductive Learning Algorithms for Complex Systems Modeling, p. 380. CRC Press, Boca Raton (1994). Chap. 5: Clusterization and Recognition"},{"key":"10_CR22","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1007\/978-3-030-16621-2_13","volume-title":"Advances in Computer Science for Engineering and Education II","author":"O Mishchuk","year":"2020","unstructured":"Mishchuk, O., Tkachenko, R., Izonin, I.: Missing data imputation through SGTM neural-like structure for environmental monitoring tasks. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2019. AISC, vol. 938, pp. 142\u2013151. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-16621-2_13"},{"key":"10_CR23","doi-asserted-by":"publisher","unstructured":"Naum, O., Chyrun, L., Vysotska, V., Kanishcheva, O.: Intellectual system design for content formation. In: Proceedings of the 12th International Scientific and Technical Conference on Computer Sciences and Information Technologies, CSIT 2017, vol. 1, pp. 131\u2013138. Institute of Electrical and Electronics Engineers Inc. (2017). https:\/\/doi.org\/10.1109\/STC-CSIT.2017.8098753","DOI":"10.1109\/STC-CSIT.2017.8098753"},{"key":"10_CR24","doi-asserted-by":"publisher","unstructured":"Ruiz, L., Pegalajar, M., Arcucci, R., Molina-Solana, M.: A time-series clustering methodology for knowledge extraction in energy consumption data. Expert Syst. Appl. 160, Article no. 113731 (2020). https:\/\/doi.org\/10.1016\/j.eswa.2020.113731","DOI":"10.1016\/j.eswa.2020.113731"},{"key":"10_CR25","doi-asserted-by":"publisher","unstructured":"Senouci, O., Harous, S., Aliouat, Z.: Survey on vehicular ad hoc networks clustering algorithms: overview, taxonomy, challenges, and open research issues. Int. J. Commun. Syst. 33(11), Article no. e4402 (2020). https:\/\/doi.org\/10.1002\/dac.4402","DOI":"10.1002\/dac.4402"},{"key":"10_CR26","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-981-15-3753-0_32","volume-title":"Proceedings of the 9th International Conference on Computer Engineering and Networks","author":"F Wang","year":"2021","unstructured":"Wang, F., Geng, Y., Zhang, H.: An improved fuzzy C-means clustering algorithm based on intuitionistic fuzzy sets. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, H.-H. (eds.) Proceedings of the 9th International Conference on Computer Engineering and Networks. AISC, vol. 1143, pp. 333\u2013345. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-3753-0_32"},{"key":"10_CR27","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ins.2020.06.069","volume":"542","author":"S Wang","year":"2021","unstructured":"Wang, S., Li, Q., Zhao, C., Zhu, X., Yuan, H., Dai, T.: Extreme clustering - a clustering method via density extreme points. Inf. Sci. 542, 24\u201339 (2021). https:\/\/doi.org\/10.1016\/j.ins.2020.06.069","journal-title":"Inf. Sci."},{"key":"10_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1007\/978-3-642-04921-7_32","volume-title":"Adaptive and Natural Computing Algorithms","author":"Q Zhao","year":"2009","unstructured":"Zhao, Q., Xu, M., Fr\u00e4nti, P.: Sum-of-squares based cluster validity index and significance analysis. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) ICANNGA 2009. LNCS, vol. 5495, pp. 313\u2013322. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04921-7_32"}],"container-title":["Communications in Computer and Information Science","Data Stream Mining & Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61656-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T22:33:33Z","timestamp":1723847613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-61656-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030616557","9783030616564"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61656-4_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DSMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Stream Mining and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ukraine","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 August 2020","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":"dsmp2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dsmp.in.ua\/","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":"easyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"36","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":"0","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":"27% - 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":"3","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":"7","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}