{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T06:45:31Z","timestamp":1777445131478,"version":"3.51.4"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030209148","type":"print"},{"value":"9783030209155","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-20915-5_38","type":"book-chapter","created":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T09:03:06Z","timestamp":1558947786000},"page":"420-430","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Determining the Eps Parameter of the DBSCAN Algorithm"],"prefix":"10.1007","author":[{"given":"Artur","family":"Starczewski","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrzej","family":"Cader","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,27]]},"reference":[{"issue":"9","key":"38_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."},{"key":"38_CR2","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1007\/978-3-319-39378-0_6","volume-title":"Artificial Intelligence and Soft Computing","author":"J Bilski","year":"2016","unstructured":"Bilski, J., Wilamowski, B.M.: Parallel learning of feedforward neural networks without error backpropagation. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNAI, vol. 9692, pp. 57\u201369. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-39378-0_6"},{"key":"38_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-319-91253-0_2","volume-title":"Artificial Intelligence and Soft Computing","author":"J Bilski","year":"2018","unstructured":"Bilski, J., Kowalczyk, B., Grzanek, K.: The parallel modification to the Levenberg-Marquardt algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS, vol. 10841, pp. 15\u201324. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-319-91253-0_2"},{"issue":"4","key":"38_CR4","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1515\/jaiscr-2017-0019","volume":"7","author":"G Bologna","year":"2017","unstructured":"Bologna, G., Hayashi, Y.: Characterization of symbolic rules embedded in deep DIMLP networks: a challenge to transparency of deep learning. J. Artif. Intell. Soft Comput. Res. 7(4), 265\u2013286 (2017)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"38_CR5","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":"38_CR6","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, pp. 9\u201315. AAAI Press, New York (1998)"},{"key":"38_CR7","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."},{"issue":"Part B","key":"38_CR8","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1016\/j.phpro.2012.02.174","volume":"24","author":"H Darong","year":"2012","unstructured":"Darong, H., Peng, W.: Grid-based DBSCAN algorithm with referential parameters. Phys. Proc. 24(Part B), 1166\u20131170 (2012)","journal-title":"Phys. Proc."},{"issue":"3","key":"38_CR9","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1515\/jaiscr-2018-0013","volume":"8","author":"G D\u2019Aniello","year":"2018","unstructured":"D\u2019Aniello, G., Gaeta, M., Loia, F., Reformat, M., Toti, D.: An environment for collective perception based on fuzzy and semantic approaches. J. Artif. Intell. Soft Comput. Res. 8(3), 191\u2013210 (2018)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"38_CR10","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":"9","key":"38_CR11","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."},{"issue":"4","key":"38_CR12","first-page":"357","volume":"24","author":"M Gabryel","year":"2018","unstructured":"Gabryel, M.: The bag-of-words method with different types of image features and dictionary analysis. J. Univ. Comput. Sci. 24(4), 357\u2013371 (2018)","journal-title":"J. Univ. Comput. Sci."},{"key":"38_CR13","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-319-99972-2_36","volume-title":"Information and Software Technologies","author":"M Gabryel","year":"2018","unstructured":"Gabryel, M.: Data analysis algorithm for click fraud recognition. In: Dama\u0161evi\u010dius, R., Vasiljevien\u0117, G. (eds.) ICIST 2018. CCIS, vol. 920, pp. 437\u2013446. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-319-99972-2_36"},{"key":"38_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1007\/978-3-319-91253-0_57","volume-title":"Artificial Intelligence and Soft Computing","author":"M Gabryel","year":"2018","unstructured":"Gabryel, M., Dama\u0161evi\u010dius, R., Przybyszewski, K.: Application of the bag-of-words algorithm in classification the quality of sales leads. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2018. LNCS, vol. 10841, pp. 615\u2013622. Springer, Cham (2018). \n                      https:\/\/doi.org\/10.1007\/978-3-319-91253-0_57"},{"key":"38_CR15","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, ICDM 2004, pp. 403\u2013406. IEEE (2004)"},{"key":"38_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."},{"issue":"2","key":"38_CR17","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1515\/jaiscr-2017-0008","volume":"7","author":"H Liu","year":"2017","unstructured":"Liu, H., Gegov, A., Cocea, M.: Rule based networks: an efficient and interpretable representation of computational models. J. Artif. Intell. Soft Comput. Res. 7(2), 111\u2013123 (2017)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"38_CR18","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."},{"issue":"3","key":"38_CR19","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1111\/1467-9868.00082","volume":"59","author":"X Meng","year":"1997","unstructured":"Meng, X., van Dyk, D.: The EM algorithm - an old folk-song sung to a fast new tune. J. Roy. Stat. Soc. Ser. B (Methodol.) 59(3), 511\u2013567 (1997)","journal-title":"J. Roy. Stat. Soc. Ser. B (Methodol.)"},{"issue":"4","key":"38_CR20","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":"7","key":"38_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."},{"issue":"1","key":"38_CR22","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1515\/jaiscr-2017-0003","volume":"7","author":"M Prasad","year":"2017","unstructured":"Prasad, M., Liu, Y.-T., Li, D.-L., Lin, C.-T., Shah, R.R., Kaiwartya, O.P.: A new mechanism for data visualization with TSK-type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33\u201346 (2017)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"issue":"2","key":"38_CR23","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1515\/jaiscr-2017-0010","volume":"7","author":"A Riid","year":"2017","unstructured":"Riid, A., Preden, J.-S.: Design of fuzzy rule-based classifiers through granulation and consolidation. J. Artif. Intell. Soft Comput. Res. 7(2), 137\u2013147 (2017)","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"38_CR24","unstructured":"Rohlf, F.: Single-link clustering algorithms. In: Krishnaiah, P.R., Kanal, L.N. (eds.) Handbook of Statistics, vol. 2, pp. 267\u2013284 (1982)"},{"issue":"1","key":"38_CR25","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."},{"issue":"1","key":"38_CR26","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1515\/jaiscr-2016-0003","volume":"6","author":"AM Serdah","year":"2016","unstructured":"Serdah, A.M., Ashour, W.M.: Clustering large-scale data based on modified affinity propagation algorithm. J. Artif. Intell. Soft Comput. Res. 6(1), 23\u201333 (2016). \n                      https:\/\/doi.org\/10.1515\/jaiscr-2016-0003","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"38_CR27","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":"38_CR28","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":"38_CR29","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":"38_CR30","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":"38_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1007\/978-3-319-39384-1_10","volume-title":"Artificial Intelligence and Soft Computing","author":"A Starczewski","year":"2016","unstructured":"Starczewski, A., Krzy\u017cak, A.: A modification of the Silhouette index for the improvement of cluster validity assessment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 114\u2013124. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-39384-1_10"},{"key":"38_CR32","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":"38_CR33","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":"38_CR34","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":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20915-5_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,27]],"date-time":"2019-05-27T09:11:04Z","timestamp":1558948264000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20915-5_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030209148","9783030209155"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20915-5_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"27 May 2019","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":"Zakopane","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Poland","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaisc2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icaisc.eu\/About","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"}},{"value":"Own online software","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"333","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"122","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"37% - 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"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}