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For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or \u2018photo-z\u2019 problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.<\/jats:p>","DOI":"10.1017\/s1743921317001569","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T07:04:51Z","timestamp":1496127891000},"page":"145-155","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Matching and Regression with Application to Photometric Redshift Estimation"],"prefix":"10.1017","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0589-6892","authenticated-orcid":false,"given":"Fionn","family":"Murtagh","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"key":"S1743921317001569_ref012","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2010.2064301"},{"key":"S1743921317001569_ref007","doi-asserted-by":"publisher","DOI":"10.1046\/j.1365-8711.2003.06271.x"},{"key":"S1743921317001569_ref015","unstructured":"Stripe 82 2016, Images Tutorial, http:\/\/www.sdss.org\/dr12\/tutorials\/get_stripe82_images\/"},{"key":"S1743921317001569_ref016","first-page":"761","volume":"423","author":"Vanzella","year":"2004","journal-title":"AandA"},{"key":"S1743921317001569_ref013","doi-asserted-by":"crossref","DOI":"10.1201\/9781315367491","volume-title":"Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics","author":"Murtagh","year":"2017"},{"key":"S1743921317001569_ref011","doi-asserted-by":"publisher","DOI":"10.1134\/S2070046616030055"},{"key":"S1743921317001569_ref010","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2005.02.002"},{"key":"S1743921317001569_ref002","doi-asserted-by":"publisher","DOI":"10.1088\/0004-6256\/144\/5\/144"},{"key":"S1743921317001569_ref001","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1086\/518864","volume":"172","author":"Adelman-McCarthy","year":"2007","journal-title":"ApJ"},{"key":"S1743921317001569_ref004","unstructured":"d\u2019Abrusco R. , Longo G. , Paolillo M. , Brescia M. , De Filippis E. , Staiano A. , & Tagliaferri R. 2006, \u201cThe use of neural networks to probe the structure of the nearby universe\u201d, Proc. 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