{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:09:19Z","timestamp":1742911759044,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030590024"},{"type":"electronic","value":"9783030590031"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-59003-1_12","type":"book-chapter","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T14:02:26Z","timestamp":1600005746000},"page":"177-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Algebra for Complex Analysis of Data"],"prefix":"10.1007","author":[{"given":"Jakub","family":"Peschel","sequence":"first","affiliation":[]},{"given":"Michal","family":"Batko","sequence":"additional","affiliation":[]},{"given":"Pavel","family":"Zezula","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"12_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-07821-2","volume-title":"Frequent Pattern Mining","year":"2014","unstructured":"Aggarwal, C.C., Han, J. (eds.): Frequent Pattern Mining. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07821-2"},{"key":"12_CR2","unstructured":"Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB. vol. 1215, pp. 487\u2013499 (1994)"},{"key":"12_CR3","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.eswa.2018.03.041","volume":"105","author":"N Aryabarzan","year":"2018","unstructured":"Aryabarzan, N., Minaei-Bidgoli, B., Teshnehlab, M.: negFIN: an efficient algorithm for fast mining frequent itemsets. Expert Syst. Appl. 105, 129\u2013143 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"12_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3091106","volume":"50","author":"T Chakraborty","year":"2017","unstructured":"Chakraborty, T., Dalmia, A., Mukherjee, A., Ganguly, N.: Metrics for community analysis: a survey. ACM Comput. Surv. (CSUR) 50(4), 1\u201337 (2017)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"12_CR5","unstructured":"Ciaccia, P., Patella, M., Zezula, P.: M-tree: An E cient access method for similarity search in metric spaces. In: Proceedings of the 23rd VLDB Conference, Athens, Greece, pp. 426\u2013435. Citeseer (1997)"},{"key":"12_CR6","unstructured":"Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a java open-source pattern mining library. J. Mach. Learn. Res. 15, 3569\u20133573 (2014). http:\/\/jmlr.org\/papers\/v15\/fournierviger14a.html"},{"key":"12_CR7","unstructured":"Gupta, M.K., Chandra, P.: A comparative study of clustering algorithms. In: 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 801\u2013805. IEEE (2019)"},{"issue":"1","key":"12_CR8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10\u201318 (2009)","journal-title":"ACM SIGKDD Explor. Newslett."},{"key":"12_CR9","doi-asserted-by":"crossref","unstructured":"Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Rec. 29, 1\u201312 (2000)","DOI":"10.1145\/335191.335372"},{"key":"12_CR10","unstructured":"Kluyver, T., et al.: Jupyter notebooks-a publishing format for reproducible computational workflows. In: ELPUB, pp. 87\u201390 (2016)"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Data sets. Cambridge university press, New York (2020)","DOI":"10.1017\/9781108684163"},{"key":"12_CR12","unstructured":"MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Mitzenmacher, M., Pagh, R., Pham, N.: Efficient estimation for high similarities using odd sketches. In: Proceedings of the 23rd International Conference on World Wide web, pp. 109\u2013118 (2014)","DOI":"10.1145\/2566486.2568017"},{"key":"12_CR14","doi-asserted-by":"crossref","unstructured":"Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 647\u2013652. ACM (2004)","DOI":"10.1145\/1014052.1014134"},{"issue":"4","key":"12_CR15","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1016\/j.is.2010.10.002","volume":"36","author":"D Novak","year":"2011","unstructured":"Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Systems 36(4), 721\u2013733 (2011)","journal-title":"Inf. Systems"},{"key":"12_CR16","unstructured":"Pei, J., et al.: Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of 17th International Conference on Data Engineering, pp. 215\u2013224. IEEE (2001)"},{"key":"12_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-030-32047-8_31","volume-title":"Similarity Search and Applications","author":"J Peschel","year":"2019","unstructured":"Peschel, J., Zezula, P.: ADAMiSS: advanced data analysis, mining and search, system. In: Amato, G., Gennaro, C., Oria, V., Radovanovi\u0107, M. (eds.) SISAP 2019. LNCS, vol. 11807, pp. 351\u2013355. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32047-8_31"},{"key":"12_CR18","series-title":"Computer Communications and Networks","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/978-1-4471-4555-4_4","volume-title":"Social Media Retrieval","author":"M Planti\u00e9","year":"2013","unstructured":"Planti\u00e9, M., Crampes, M.: Survey on social community detection. In: Ramzan, N., van Zwol, R., Lee, J.S., Cl\u00faver, K., Hua, X.S. (eds.) Social Media Retrieval. CCN, pp. 65\u201385. Springer, Lodon (2013). https:\/\/doi.org\/10.1007\/978-1-4471-4555-4_4"},{"key":"12_CR19","unstructured":"Schubert, E., Zimek, A.: ELKI: a large open-source library for data analysis - ELKI release 0.7.5 \u201cheidelberg\u201d. CoRR abs\/1902.03616 (2019). http:\/\/arxiv.org\/abs\/1902.03616"},{"key":"12_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BFb0014140","volume-title":"Advances in Database Technology \u2014 EDBT 1996","author":"R Srikant","year":"1996","unstructured":"Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1\u201317. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/BFb0014140"},{"key":"12_CR21","unstructured":"Team, R.C., et al.: R: a language and environment for statistical computing (2013)"},{"key":"12_CR22","unstructured":"Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: 2002 IEEE International Conference on Data Mining, 2002. Proceedings, pp. 721\u2013724. IEEE (2002)"},{"issue":"1\u20132","key":"12_CR23","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1023\/A:1007652502315","volume":"42","author":"MJ Zaki","year":"2001","unstructured":"Zaki, M.J.: Spade: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1\u20132), 31\u201360 (2001)","journal-title":"Mach. Learn."},{"issue":"3","key":"12_CR24","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/69.846291","volume":"12","author":"MJ Zaki","year":"2000","unstructured":"Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372\u2013390 (2000)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59003-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:19:17Z","timestamp":1710339557000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59003-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030590024","9783030590031"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59003-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"14 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","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":"14 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2020","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"190","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":"38","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":"20","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":"20% - 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":"4-6","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":"3-4","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)"}},{"value":"Due to the COVID-19 pandemic the conference was held online. DEXA Workshops volume: submissions sent - 15, full papers accepted - 6, short papers accepted - 4, reviewers per paper 3, papers per reviewer 1-2","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)"}}]}}