{"id":"https://openalex.org/W3158004143","doi":"https://doi.org/10.1109/hipc50609.2020.00028","title":"Parallel Hierarchical Clustering using Rank-Two Nonnegative Matrix Factorization","display_name":"Parallel Hierarchical Clustering using Rank-Two Nonnegative Matrix Factorization","publication_year":2020,"publication_date":"2020-12-01","ids":{"openalex":"https://openalex.org/W3158004143","doi":"https://doi.org/10.1109/hipc50609.2020.00028","mag":"3158004143"},"language":"en","primary_location":{"id":"doi:10.1109/hipc50609.2020.00028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipc50609.2020.00028","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC)","raw_type":"proceedings-article"},"type":"article","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/A5064605456","display_name":"Lawton Manning","orcid":null},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lawton Manning","raw_affiliation_strings":["Wake Forest University, Winston-Salem, NC, USA"],"affiliations":[{"raw_affiliation_string":"Wake Forest University, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064243391","display_name":"Grey Ballard","orcid":"https://orcid.org/0000-0003-1557-8027"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Grey Ballard","raw_affiliation_strings":["Wake Forest University, Winston-Salem, NC, USA"],"affiliations":[{"raw_affiliation_string":"Wake Forest University, Winston-Salem, NC, USA","institution_ids":["https://openalex.org/I47251452"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014168997","display_name":"Ramakrishnan Kannan","orcid":"https://orcid.org/0000-0002-5852-4806"},"institutions":[{"id":"https://openalex.org/I1289243028","display_name":"Oak Ridge National Laboratory","ror":"https://ror.org/01qz5mb56","country_code":"US","type":"facility","lineage":["https://openalex.org/I1289243028","https://openalex.org/I1330989302","https://openalex.org/I39565521","https://openalex.org/I4210159294"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramakrishnan Kannan","raw_affiliation_strings":["Oak Ridge National Laboratory, Oak Ridge, TN, USA"],"affiliations":[{"raw_affiliation_string":"Oak Ridge National Laboratory, Oak Ridge, TN, USA","institution_ids":["https://openalex.org/I1289243028"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101728710","display_name":"Haesun Park","orcid":"https://orcid.org/0000-0001-6259-7170"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haesun Park","raw_affiliation_strings":["Georgia Institute of Technology, Atlanta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology, Atlanta, GA, USA","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5064605456"],"corresponding_institution_ids":["https://openalex.org/I47251452"],"apc_list":null,"apc_paid":null,"fwci":0.3773,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.70314197,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"141","last_page":"150"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9961000084877014,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9830999970436096,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9399999976158142,"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/computer-science","display_name":"Computer science","score":0.7389639019966125},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7124320864677429},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.5721973776817322},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.48506084084510803},{"id":"https://openalex.org/keywords/supercomputer","display_name":"Supercomputer","score":0.4718981683254242},{"id":"https://openalex.org/keywords/canopy-clustering-algorithm","display_name":"Canopy clustering algorithm","score":0.4694518446922302},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.46788519620895386},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.45682528614997864},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.42995545268058777},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.4200674891471863},{"id":"https://openalex.org/keywords/sparse-matrix","display_name":"Sparse matrix","score":0.4192211329936981},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.41748136281967163},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.41180577874183655},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3940946161746979},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3745190501213074},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3617281913757324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26403653621673584},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17408493161201477}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7389639019966125},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7124320864677429},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.5721973776817322},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.48506084084510803},{"id":"https://openalex.org/C83283714","wikidata":"https://www.wikidata.org/wiki/Q121117","display_name":"Supercomputer","level":2,"score":0.4718981683254242},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.4694518446922302},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.46788519620895386},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.45682528614997864},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.42995545268058777},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.4200674891471863},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.4192211329936981},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.41748136281967163},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.41180577874183655},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3940946161746979},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3745190501213074},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3617281913757324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26403653621673584},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17408493161201477},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hipc50609.2020.00028","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hipc50609.2020.00028","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 27th International Conference on High Performance Computing, Data, and Analytics (HiPC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3873352507","display_name":null,"funder_award_id":"OAC-1642385,OAC-1642410","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W2013029404","https://openalex.org/W2072191009","https://openalex.org/W2077583079","https://openalex.org/W2093492509","https://openalex.org/W2095088894","https://openalex.org/W2099611016","https://openalex.org/W2113359929","https://openalex.org/W2131613942","https://openalex.org/W2144351558","https://openalex.org/W2170796499","https://openalex.org/W2285339552","https://openalex.org/W2296132099","https://openalex.org/W2524210471","https://openalex.org/W2535045667","https://openalex.org/W2571268788","https://openalex.org/W2605123798","https://openalex.org/W2971526651","https://openalex.org/W3081111652","https://openalex.org/W3099395583","https://openalex.org/W3173185726","https://openalex.org/W6697177954","https://openalex.org/W6890300178"],"related_works":["https://openalex.org/W2559422900","https://openalex.org/W3144143113","https://openalex.org/W4306940721","https://openalex.org/W2160785859","https://openalex.org/W4301002638","https://openalex.org/W2892323093","https://openalex.org/W3022637481","https://openalex.org/W4220814143","https://openalex.org/W2384052049","https://openalex.org/W2087424554"],"abstract_inverted_index":{"Nonnegative":[0],"Matrix":[1],"Factorization":[2],"(NMF)":[3],"is":[4],"an":[5],"effective":[6],"tool":[7],"for":[8,13,22,36,83,170],"clustering":[9,38,159,185],"nonnegative":[10],"data,":[11],"either":[12],"computing":[14],"a":[15,19,24,33,41,50,69],"flat":[16,70],"partitioning":[17],"of":[18,26,109,131,137,146,152,179],"dataset":[20],"or":[21],"determining":[23],"hierarchy":[25,108],"similarity.":[27],"In":[28],"this":[29,60],"paper,":[30],"we":[31],"propose":[32],"parallel":[34,133],"algorithm":[35,117,134,169],"hierarchical":[37,158,184],"that":[39],"uses":[40],"divide-and-conquer":[42],"approach":[43,61,160],"based":[44],"on":[45,175,196],"rank-two":[46,75],"NMF":[47,71,76,172],"to":[48,90,100,118,120,141,149,161,177,193],"split":[49],"data":[51,67,93,125,138],"set":[52],"into":[53],"two":[54],"cohesive":[55],"parts.":[56],"Not":[57],"only":[58],"does":[59],"uncover":[62],"more":[63,80],"structure":[64],"in":[65,135],"the":[66,106,116,129,153,157,182,197],"than":[68,82],"clustering,":[72],"but":[73],"also":[74],"can":[77],"be":[78],"computed":[79],"quickly":[81],"general":[84],"ranks,":[85],"providing":[86],"comparable":[87],"overall":[88],"time":[89],"solution.":[91],"Our":[92,168],"distribution":[94],"and":[95,123,144,164,181],"parallelization":[96],"strategies":[97],"are":[98],"designed":[99],"maintain":[101],"computational":[102],"load":[103],"balance":[104],"throughout":[105],"data-dependent":[107],"computation":[110],"while":[111],"limiting":[112],"interprocess":[113],"communication,":[114],"allowing":[115],"scale":[119],"large":[121],"dense":[122],"sparse":[124],"sets.":[126],"We":[127],"demonstrate":[128],"scalability":[130],"our":[132],"terms":[136],"size":[139],"(up":[140,148],"800":[142,198],"GB)":[143],"number":[145],"processors":[147],"80":[150,194],"nodes":[151,195],"Summit":[154],"supercomputer),":[155],"applying":[156],"hyperspectral":[162],"imaging":[163],"image":[165],"classification":[166],"data.":[167],"Rank-2":[171],"scales":[173],"perfectly":[174],"up":[176],"1000s":[178],"cores":[180],"entire":[183],"method":[186],"achieves":[187],"5.9x":[188],"speedup":[189],"scaling":[190],"from":[191],"10":[192],"GB":[199],"dataset.":[200]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
