{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:11:22Z","timestamp":1743826282200,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887192","type":"print"},{"value":"9783031887208","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-88720-8_30","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:10:24Z","timestamp":1743768624000},"page":"184-189","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combining Dissimilarity Spaces to\u00a0Improve Approximate Similarity Search"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2360-0394","authenticated-orcid":false,"given":"Elena","family":"Garc\u00eda-Morato","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0231-2051","authenticated-orcid":false,"given":"Felipe","family":"Ortega","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6434-7263","authenticated-orcid":false,"given":"Javier","family":"G\u00f3mez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"30_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-44503-X_27","volume-title":"Database Theory \u2014 ICDT 2001","author":"CC Aggarwal","year":"2001","unstructured":"Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420\u2013434. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44503-X_27"},{"key":"30_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2019.02.006","volume":"87","author":"M Aum\u00fcller","year":"2020","unstructured":"Aum\u00fcller, M., Bernhardsson, E., Faithfull, A.: ANN-Benchmarks: a benchmarking tool for approximate nearest neighbor algorithms. Inf. Syst. 87, 101374 (2020). https:\/\/doi.org\/10.1016\/j.is.2019.02.006","journal-title":"Inf. Syst."},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is \u201dnearest neighbor\u201d meaningful? In: Beeri, C., Buneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217\u2013235. Springer, Heidelberg (1999). https:\/\/doi.org\/10.1007\/3-540-49257-7_15","DOI":"10.1007\/3-540-49257-7_15"},{"key":"30_CR4","doi-asserted-by":"publisher","unstructured":"Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200\u2013210 (2013). https:\/\/doi.org\/10.1016\/j.eswa.2012.07.021","DOI":"10.1016\/j.eswa.2012.07.021"},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Ehsani, R., Drabl\u00f8s, F.: Robust distance measures for k NN classification of cancer data. Cancer Inform. 19, 117693512096554 (2020). https:\/\/doi.org\/10.1177\/1176935120965542","DOI":"10.1177\/1176935120965542"},{"key":"30_CR6","unstructured":"Garcia-Morato, E., Algar, M.J., Alfaro, C., Ortega, F., Gomez, J., Moguerza, J.M.: A general framework for distributed approximate similarity search with arbitrary distances (2024)"},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing - STOC 1998, Dallas, Texas, United States, pp. 604\u2013613. ACM Press (1998). https:\/\/doi.org\/10.1145\/276698.276876","DOI":"10.1145\/276698.276876"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Series in Probability and Statistics, 1st edn. Wiley, Hoboken (1990). https:\/\/doi.org\/10.1002\/9780470316801","DOI":"10.1002\/9780470316801"},{"issue":"8","key":"30_CR9","doi-asserted-by":"publisher","first-page":"1475","DOI":"10.1109\/TKDE.2019.2909204","volume":"32","author":"W Li","year":"2020","unstructured":"Li, W., et al.: Approximate nearest neighbor search on high dimensional data \u2013 experiments, analyses, and improvement. IEEE Trans. Knowl. Data Eng. 32(8), 1475\u20131488 (2020). https:\/\/doi.org\/10.1109\/TKDE.2019.2909204","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169\u2013198 (1999). https:\/\/doi.org\/10.1613\/jair.614","DOI":"10.1613\/jair.614"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications, Series in Machine Perception and Artificial Intelligence, vol.\u00a064. World Scientific, Hackensack (2005). https:\/\/doi.org\/10.1142\/5965","DOI":"10.1142\/5965"},{"key":"30_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/3-540-48219-9_36","volume-title":"Multiple Classifier Systems","author":"E P\u0119kalska","year":"2001","unstructured":"P\u0119kalska, E., Duin, R.: On combining dissimilarity representations. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 359\u2013368. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-48219-9_36"},{"key":"30_CR13","unstructured":"Samet, H.: Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann Series in Data Management Systems, Academic Press, San Francisco (2006)"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197\u2013227 (1990). https:\/\/doi.org\/10.1007\/BF00116037. http:\/\/link.springer.com\/10.1007\/BF00116037","DOI":"10.1007\/BF00116037"},{"key":"30_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2020.101507","volume":"95","author":"LC Shimomura","year":"2021","unstructured":"Shimomura, L.C., Oyamada, R.S., Vieira, M.R., Kaster, D.S.: A survey on graph-based methods for similarity searches in metric spaces. Inf. Syst. 95, 101507 (2021). https:\/\/doi.org\/10.1016\/j.is.2020.101507","journal-title":"Inf. Syst."},{"key":"30_CR16","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1371","volume":"9","author":"S Shvydun","year":"2023","unstructured":"Shvydun, S.: Models of similarity in complex networks. PeerJ Comput. Sci. 9, e1371 (2023). https:\/\/doi.org\/10.7717\/peerj-cs.1371","journal-title":"PeerJ Comput. Sci."},{"key":"30_CR17","unstructured":"Singhal, A.: Modern information retrieval: a brief overview. Bull. IEEE Comput. Soc. Tech. Committee Data Eng. 24(4), 35\u201343 (2001). http:\/\/singhal.info\/ieee2001.pdf"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Skopal, T., Bustos, B.: On nonmetric similarity search problems in complex domains. ACM Comput. Surv. 43(4) (2011). https:\/\/doi.org\/10.1145\/1978802.1978813","DOI":"10.1145\/1978802.1978813"},{"key":"30_CR19","unstructured":"Wang, Z., Wang, P., Palpanas, T., Wang, W.: Graph- and tree-based indexes for high-dimensional vector similarity search: analyses, comparisons, and future directions. IEEE Data Eng. Bull. 46(3), 3\u201321 (2023). http:\/\/sites.computer.org\/debull\/A23sept\/p3.pdf"},{"key":"30_CR20","doi-asserted-by":"publisher","unstructured":"Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms, 0 edn. Chapman and Hall\/CRC (2012). https:\/\/doi.org\/10.1201\/b12207","DOI":"10.1201\/b12207"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88720-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:10:31Z","timestamp":1743768631000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88720-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887192","9783031887208"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88720-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}