{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T09:50:06Z","timestamp":1766137806139,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030879853"},{"type":"electronic","value":"9783030879860"}],"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\/978-3-030-87986-0_32","type":"book-chapter","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T11:10:30Z","timestamp":1633432230000},"page":"358-368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Novel Approach to Determining the Radius of the Neighborhood Required for the DBSCAN Algorithm"],"prefix":"10.1007","author":[{"given":"Artur","family":"Starczewski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"issue":"9","key":"32_CR1","doi-asserted-by":"publisher","first-page":"2561","DOI":"10.1109\/TPDS.2014.2357019","volume":"26","author":"J Bilski","year":"2015","unstructured":"Bilski, J., Smol\u0105g, J.: Parallel architectures for learning the RTRN and Elman dynamic neural networks. IEEE Trans. Parallel Distrib. Syst. 26(9), 2561\u20132570 (2015)","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"4","key":"32_CR2","doi-asserted-by":"publisher","first-page":"299","DOI":"10.2478\/jaiscr-2020-0020","volume":"10","author":"J Bilski","year":"2020","unstructured":"Bilski, J., Kowalczyk, B., Marchlewska, A., Zurada, J.M.: Local levenberg-marquardt algorithm for learning feedforward neural networks. J. Artif. Intell. Soft Comput. Res. 10(4), 299\u2013316 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"32_CR3","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.patcog.2019.01.034","volume":"90","author":"T Boonchoo","year":"2019","unstructured":"Boonchoo, T., Ao, X., Liu, Y., Zhao, W., He, Q.: Grid-based DBSCAN: indexing and inference. Pattern Recogn. 90, 271\u2013284 (2019)","journal-title":"Pattern Recogn."},{"key":"32_CR4","unstructured":"Bradley P., Fayyad U.: Refining initial points for k-means clustering. In: Proceedings of the Fifteenth International Conference on Knowledge Discovery and Data Mining, New York, AAAI Press, pp. 9\u201315 (1998)"},{"key":"32_CR5","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.patcog.2018.05.030","volume":"83","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Tang, S., Bouguila, N., Wanga, C., Du, J., Li, H.: A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data. Pattern Recogn. 83, 375\u2013387 (2018)","journal-title":"Pattern Recogn."},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Darong H., Peng W.: Grid-based dbscan algorithm with referential parameters. Phys. Procedia 24, Part B, 1166\u20131170 (2012)","DOI":"10.1016\/j.phpro.2012.02.174"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Dziwin\u0307ski, P., Bartczuk, \u0141, Paszkowski, J.: A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm. J. Artif. Intell. Soft Comput. Res. 10(2), 95\u2013111 (2020)","DOI":"10.2478\/jaiscr-2020-0007"},{"key":"32_CR8","unstructured":"Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceeding of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226\u2013231 (1996)"},{"issue":"2","key":"32_CR9","doi-asserted-by":"publisher","first-page":"99","DOI":"10.2478\/jaiscr-2018-0027","volume":"9","author":"M Ferdaus","year":"2019","unstructured":"Ferdaus, M., Anavatti, S.G., Garratt, M.A., Pratama, M.: Development of C-means clustering based adaptive fuzzy controller for a flapping wing micro air vehicle. J. Artif. Intell. Soft Comput. Res. 9(2), 99\u2013109 (2019)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"9","key":"32_CR10","doi-asserted-by":"publisher","first-page":"3034","DOI":"10.1016\/j.patcog.2014.03.017","volume":"47","author":"P Fr\u00e4nti","year":"2014","unstructured":"Fr\u00e4nti, P., Rezaei, M., Zhao, Q.: Centroid index: cluster level similarity measure. Pattern Recogn. 47(9), 3034\u20133045 (2014)","journal-title":"Pattern Recogn."},{"key":"32_CR11","first-page":"437","volume":"920","author":"M Gabryel","year":"2018","unstructured":"Gabryel, M.: Data analysis algorithm for click fraud recognition. Commun. Comput. Inf. Sci. 920, 437\u2013446 (2018)","journal-title":"Commun. Comput. Inf. Sci."},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Ga\u0142kowski, T., Krzyak, A., Filutowicz, Z.: A new approach to detection of changes in multidimensional patterns. J. Artif. Intell. Soft Comput. Res. 10(2), 125\u2013136 (2020)","DOI":"10.2478\/jaiscr-2020-0009"},{"issue":"2","key":"32_CR13","doi-asserted-by":"publisher","first-page":"113","DOI":"10.2478\/jaiscr-2020-0008","volume":"10","author":"R Grycuk","year":"2020","unstructured":"Grycuk, R., Najgebauer, P., Kordos, M., Scherer, M., Marchlewska, A.: Fast image index for database management engines. J. Artif. Intell. Soft Comput. Res. 10(2), 113\u2013123 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Hruschka E.R., de Castro L.N., Campello R.J.: Evolutionary algorithms for clustering gene-expression data, In: Fourth IEEE International Conference on Data Mining, 2004, ICDM 2004, pp. 403\u2013406. IEEE (2004)","DOI":"10.1109\/ICDM.2004.10073"},{"key":"32_CR15","volume-title":"Algorithms for clustering data","author":"A Jain","year":"1988","unstructured":"Jain, A., Dubes, R.: Algorithms for clustering data. Prentice-Hall, Englewood Cliffs (1988)"},{"key":"32_CR16","first-page":"1","volume":"91","author":"A Karami","year":"2014","unstructured":"Karami, A., Johansson, R.: Choosing DBSCAN parameters automatically using differential evolution. Int. J. Comput. Appl. 91, 1\u201311 (2014)","journal-title":"Int. J. Comput. Appl."},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.patrec.2018.12.010","volume":"117","author":"D Luchi","year":"2019","unstructured":"Luchi, D., Rodrigues, A.L., Varejao, F.M.: Sampling approaches for applying DBSCAN to large datasets. Pattern Recogn. Lett. 117, 90\u201396 (2019)","journal-title":"Pattern Recogn. Lett."},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Meng X., van Dyk D.: The EM algorithm - An old folk-song sung to a fast new tune. J. Royal Stat. Soc. Series B (Methodological) 59(3), 511\u2013567 (1997)","DOI":"10.1111\/1467-9868.00082"},{"issue":"4","key":"32_CR19","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1093\/comjnl\/26.4.354","volume":"26","author":"F Murtagh","year":"1983","unstructured":"Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26(4), 354\u2013359 (1983)","journal-title":"Comput. J."},{"issue":"1","key":"32_CR20","doi-asserted-by":"publisher","first-page":"47","DOI":"10.2478\/jaiscr-2020-0004","volume":"10","author":"R Nowicki","year":"2020","unstructured":"Nowicki, R., Grzanek, K., Hayashi, Y.: Rough support vector machine for classification with interval and incomplete data. J. Artif. Intell. Soft Comput. Res. 10(1), 47\u201356 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"7","key":"32_CR21","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1109\/TKDE.2006.106","volume":"18","author":"A Patrikainen","year":"2006","unstructured":"Patrikainen, A., Meila, M.: Comparing subspace clusterings. IEEE Trans. Knowl. Data Eng. 18(7), 902\u2013916 (2006)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Rohlf, F.: Single-link clustering algorithms. In: Krishnaiah, P.R., Kanal, L.N. (eds.), Handbook of Statistics, vol. 2, pp. 267\u2013284 (1982)","DOI":"10.1016\/S0169-7161(82)02015-X"},{"issue":"1","key":"32_CR23","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10044-007-0099-1","volume":"12","author":"AS Sameh","year":"2009","unstructured":"Sameh, A.S., Asoke, K.N.: Development of assessment criteria for clustering algorithms. Pattern Anal. Appl. 12(1), 79\u201398 (2009)","journal-title":"Pattern Anal. Appl."},{"key":"32_CR24","doi-asserted-by":"crossref","unstructured":"Shah, G.H.: An improved dbscan, a density based clustering algorithm with parameter selection for high dimensional data sets. In: Nirma University International Engineering, (NUiCONE), pp. 1\u20136 (2012)","DOI":"10.1109\/NUICONE.2012.6493211"},{"issue":"3\u20134","key":"32_CR25","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s007780050009","volume":"8","author":"G Sheikholeslam","year":"2000","unstructured":"Sheikholeslam, G., Chatterjee, S., Zhang, A.: WaveCluster: a wavelet-based clustering approach for spatial data in very large databases. Int. J. Very Large Data Bases 8(3\u20134), 289\u2013304 (2000)","journal-title":"Int. J. Very Large Data Bases"},{"key":"32_CR26","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.asoc.2014.05.001","volume":"22","author":"H-L Shieh","year":"2014","unstructured":"Shieh, H.-L.: Robust validity index for a modified subtractive clustering algorithm. Appl. Soft Comput. 22, 47\u201359 (2014)","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"32_CR27","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1007\/s10044-015-0525-8","volume":"20","author":"A Starczewski","year":"2017","unstructured":"Starczewski, A.: A new validity index for crisp clusters. Pattern Anal. Appl. 20(3), 687\u2013700 (2017)","journal-title":"Pattern Anal. Appl."},{"key":"32_CR28","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/978-3-030-20915-5_38","volume-title":"Artificial Intelligence and Soft Computing","author":"A Starczewski","year":"2019","unstructured":"Starczewski, A., Cader, A.: Determining the Eps parameter of the DBSCAN algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11509, pp. 420\u2013430. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20915-5_38"},{"issue":"4","key":"32_CR29","doi-asserted-by":"publisher","first-page":"271","DOI":"10.2478\/jaiscr-2020-0018","volume":"10","author":"J Starczewski","year":"2020","unstructured":"Starczewski, J., Goetzen, P., Napoli, C.: Triangular fuzzy-rough set based fuzzification of fuzzy rule-based systems. J. Artif. Intell. Soft Comput. Res. 10(4), 271\u2013285 (2020)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"32_CR30","unstructured":"Wang, W., Yang, J., Muntz, R.: STING: a statistical information grid approach to spatial data mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, VLDB 1997, pp. 186\u2013195 (1997)"},{"issue":"16","key":"32_CR31","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1016\/j.patrec.2009.08.008","volume":"30","author":"P Viswanath","year":"2009","unstructured":"Viswanath, P., Suresh Babu, V.S.: Rough-dbscan: a fast hybrid density based clustering method for large data sets. Pattern Recogn. Lett. 30(16), 1477\u20131488 (2009)","journal-title":"Pattern Recogn. Lett."},{"issue":"9","key":"32_CR32","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1016\/j.patrec.2008.02.014","volume":"29","author":"KR Zalik","year":"2008","unstructured":"Zalik, K.R.: An efficient k-means clustering algorithm. Pattern Recogn. Lett. 29(9), 1385\u20131391 (2008)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87986-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T09:45:40Z","timestamp":1725875140000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87986-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030879853","9783030879860"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87986-0_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"5 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAISC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence and Soft Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaisc2021.icaisc.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Own Online System","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"195","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":"89","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":"46% - 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":"3","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)"}}]}}