{"id":"https://openalex.org/W2081986312","doi":"https://doi.org/10.1145/2783258.2788574","title":"Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering","display_name":"Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering","publication_year":2015,"publication_date":"2015-08-07","ids":{"openalex":"https://openalex.org/W2081986312","doi":"https://doi.org/10.1145/2783258.2788574","mag":"2081986312"},"language":"en","primary_location":{"id":"doi:10.1145/2783258.2788574","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2783258.2788574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062677262","display_name":"Shigeru Maya","orcid":"https://orcid.org/0000-0002-5314-3203"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeru Maya","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114115269","display_name":"Kai Morino","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kai Morino","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114752541","display_name":"Hiroshi Murata","orcid":"https://orcid.org/0000-0001-8004-991X"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Murata","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042150834","display_name":"Ryo Asaoka","orcid":"https://orcid.org/0000-0001-7182-1912"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Ryo Asaoka","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021981442","display_name":"Kenji Yamanishi","orcid":"https://orcid.org/0000-0001-7370-9991"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenji Yamanishi","raw_affiliation_strings":["The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":null,"apc_paid":null,"fwci":16.7361,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.98650052,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1979","last_page":"1988"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10250","display_name":"Glaucoma and retinal disorders","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10250","display_name":"Glaucoma and retinal disorders","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.9781000018119812,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.7163432240486145},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7144311666488647},{"id":"https://openalex.org/keywords/glaucoma","display_name":"Glaucoma","score":0.693661630153656},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6922227740287781},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6401585936546326},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5121760368347168},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4223489761352539},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.0655796229839325}],"concepts":[{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.7163432240486145},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7144311666488647},{"id":"https://openalex.org/C2778527774","wikidata":"https://www.wikidata.org/wiki/Q159701","display_name":"Glaucoma","level":2,"score":0.693661630153656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6922227740287781},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6401585936546326},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5121760368347168},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4223489761352539},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0655796229839325},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2783258.2788574","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2783258.2788574","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306709","display_name":"Glaucoma Research Foundation","ror":"https://ror.org/05ez53b31"},{"id":"https://openalex.org/F4320322832","display_name":"University of Tokyo","ror":"https://ror.org/057zh3y96"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1519109293","https://openalex.org/W1546703457","https://openalex.org/W1820257239","https://openalex.org/W1983873315","https://openalex.org/W2006546373","https://openalex.org/W2034230784","https://openalex.org/W2068782468","https://openalex.org/W2081398938","https://openalex.org/W2088505197","https://openalex.org/W2097645701","https://openalex.org/W2098318489","https://openalex.org/W2124386111","https://openalex.org/W2131067207","https://openalex.org/W2140591816","https://openalex.org/W2152523756","https://openalex.org/W2161682962","https://openalex.org/W2161969291","https://openalex.org/W2322015057","https://openalex.org/W2330388996","https://openalex.org/W2573934436","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2082087664","https://openalex.org/W2070104888","https://openalex.org/W4285351923","https://openalex.org/W4389542577","https://openalex.org/W2885840481","https://openalex.org/W4240661191","https://openalex.org/W1998930228","https://openalex.org/W2038649333","https://openalex.org/W3096540609","https://openalex.org/W3200375535"],"abstract_inverted_index":{"In":[0,85],"this":[1,49,56,79],"paper,":[2],"we":[3,47,59,70,96,198],"propose":[4,71],"a":[5,72,155],"method":[6,76,149,161,178],"to":[7,19,77,89,130,206,226],"cluster":[8,100],"the":[9,13,21,31,62,67,82,90,98,104,111,116,125,131,134,137,145,171,180,208],"spacial":[10],"patterns":[11,23,183,192],"of":[12,24,28,34,136,147,184,189,210,220],"visual":[14,32,68],"field":[15,33],"in":[16,30,55,66,133,175,229],"glaucoma":[17,35,157,173,185,212,230],"patients":[18,36],"analyze":[20],"progression":[22,29],"glaucoma.":[25],"The":[26],"degree":[27],"can":[37,60,122,203,224],"be":[38,204],"divided":[39],"into":[40,81],"several":[41],"regions":[42],"by":[43],"straight":[44],"line":[45],"boundaries,":[46],"call":[48],"specific":[50,187],"structure":[51,65,80,118,140],"Direct":[52],"Product":[53],"Structure":[54],"paper.":[57],"Since":[58],"observe":[61],"direct":[63,138],"product":[64,139],"fields,":[69],"bottom-up":[73],"hierarchical":[74],"clustering":[75,83,117],"embed":[78],"structure.":[84],"our":[86,148,177,201,215],"method,":[87],"according":[88],"minimum":[91],"description":[92],"length":[93,107],"(MDL)":[94],"principle,":[95],"select":[97,124],"best":[99],"division":[101],"so":[102],"that":[103,127,200],"total":[105],"code":[106],"required":[108],"for":[109,141,167],"encoding":[110],"data":[112],"as":[113,115,186],"well":[114],"is":[119],"minimum.":[120],"We":[121,143],"thereby":[123],"clusters":[126,202,216],"are":[128],"robust":[129],"noise":[132],"position":[135],"clustering.":[142],"demonstrate":[144],"effectiveness":[146],"using":[150],"an":[151],"artificial":[152],"dataset":[153,174],"and":[154,222],"real":[156,172],"dataset.":[158],"Our":[159],"proposed":[160],"performed":[162],"better":[163],"than":[164],"existing":[165],"methods":[166],"both":[168],"datasets.":[169],"For":[170],"particular,":[176],"discovered":[179],"characteristic":[181],"progressive":[182],"features":[188],"clusters.":[190],"These":[191],"agree":[193],"with":[194],"clinical":[195],"knowledge.":[196],"Furthermore,":[197],"show":[199],"applied":[205],"improve":[207],"accuracy":[209],"predicting":[211],"progression.":[213],"Thus,":[214],"contain":[217],"rich":[218],"information":[219],"glaucoma,":[221],"hence":[223],"contribute":[225],"further":[227],"development":[228],"research.":[231]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}