{"id":"https://openalex.org/W3024335163","doi":"https://doi.org/10.1109/coginfocom47531.2019.9089906","title":"Machine preparation for human labelling of hierarchical train sets by spectral clustering","display_name":"Machine preparation for human labelling of hierarchical train sets by spectral clustering","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W3024335163","doi":"https://doi.org/10.1109/coginfocom47531.2019.9089906","mag":"3024335163"},"language":"en","primary_location":{"id":"doi:10.1109/coginfocom47531.2019.9089906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coginfocom47531.2019.9089906","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","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/A5046406057","display_name":"D\u00e1vid Papp","orcid":"https://orcid.org/0000-0002-8814-2745"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"David Papp","raw_affiliation_strings":["Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076485961","display_name":"G\u00e1bor Sz\u00fccs","orcid":"https://orcid.org/0000-0002-5781-1088"},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Gabor Szucs","raw_affiliation_strings":["Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary"],"affiliations":[{"raw_affiliation_string":"Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017417574","display_name":"Zsolt Knoll","orcid":null},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Zsolt Knoll","raw_affiliation_strings":["Balatonf\u00fcred Student Research Group, Budapest University of Technology and Economics, Balatonf\u00fcred, Hungary"],"affiliations":[{"raw_affiliation_string":"Balatonf\u00fcred Student Research Group, Budapest University of Technology and Economics, Balatonf\u00fcred, Hungary","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5046406057"],"corresponding_institution_ids":["https://openalex.org/I29770179"],"apc_list":null,"apc_paid":null,"fwci":0.3317,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.59807291,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"15","issue":null,"first_page":"157","last_page":"162"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12292","display_name":"Graph Theory and Algorithms","score":0.9883000254631042,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9847000241279602,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7670854926109314},{"id":"https://openalex.org/keywords/spectral-clustering","display_name":"Spectral clustering","score":0.6624721884727478},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.654740571975708},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6127743124961853},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5344300270080566},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5200445652008057},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.44865673780441284},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44556087255477905},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4372090697288513},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.41205573081970215},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34297674894332886},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2835645377635956},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.25024569034576416}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7670854926109314},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.6624721884727478},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654740571975708},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6127743124961853},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5344300270080566},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5200445652008057},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.44865673780441284},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44556087255477905},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4372090697288513},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.41205573081970215},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34297674894332886},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2835645377635956},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.25024569034576416},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coginfocom47531.2019.9089906","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coginfocom47531.2019.9089906","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 10th IEEE International Conference on Cognitive Infocommunications (CogInfoCom)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W65833265","https://openalex.org/W104553443","https://openalex.org/W1128809682","https://openalex.org/W1130686896","https://openalex.org/W1481933820","https://openalex.org/W1546503963","https://openalex.org/W1550206324","https://openalex.org/W1908985308","https://openalex.org/W1959228470","https://openalex.org/W1971415293","https://openalex.org/W2001427579","https://openalex.org/W2036328877","https://openalex.org/W2127218421","https://openalex.org/W2132914434","https://openalex.org/W2147238549","https://openalex.org/W2149684865","https://openalex.org/W2151103935","https://openalex.org/W2185859000","https://openalex.org/W2219898335","https://openalex.org/W2277761648","https://openalex.org/W2282091814","https://openalex.org/W2540269248","https://openalex.org/W2562008950","https://openalex.org/W2580221632","https://openalex.org/W2622944721","https://openalex.org/W2783307298","https://openalex.org/W2888320113","https://openalex.org/W2913464721","https://openalex.org/W2915038932","https://openalex.org/W2915431448","https://openalex.org/W2955452307","https://openalex.org/W2963358591","https://openalex.org/W4206434639","https://openalex.org/W4242399520","https://openalex.org/W4254185350","https://openalex.org/W4255130329","https://openalex.org/W4299345493","https://openalex.org/W6632865047","https://openalex.org/W6678914141","https://openalex.org/W6686508619","https://openalex.org/W6732646663","https://openalex.org/W6836092167","https://openalex.org/W6836931785"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W2393816671","https://openalex.org/W2158836806","https://openalex.org/W3200375535","https://openalex.org/W1482912984","https://openalex.org/W1989705029"],"abstract_inverted_index":{"Human":[0],"labeling":[1],"of":[2,17,26,60,66,101,115,164],"an":[3],"unknown":[4],"dataset":[5],"for":[6,13],"machine":[7,24],"learning":[8],"is":[9,74,88,121],"a":[10,23,77,107,123],"tedious":[11],"work":[12],"humans.":[14],"The":[15,52,71,143],"aim":[16],"the":[18,27,36,58,64,96,99,102,112,128,162],"paper":[19],"was":[20,55],"to":[21,33,46,56,62,90,154],"develop":[22],"preparation":[25],"data":[28],"that":[29,146],"helps":[30],"human":[31],"annotators":[32],"label":[34],"at":[35,139],"higher":[37],"level":[38],"(point-set":[39],"level).":[40],"We":[41,134],"present":[42],"two":[43,140],"different":[44,141],"approaches":[45,138],"cluster":[47],"point-sets":[48,116,131],"with":[49,149],"spectral":[50,147],"clustering.":[51,103],"fundamental":[53],"idea":[54],"use":[57,155],"set":[59],"relations":[61],"rescale":[63],"weight":[65,80,117,150],"edges":[67],"between":[68,130],"point":[69,87],"pairs.":[70],"first":[72],"approach":[73,105],"based":[75],"on":[76,156],"fully":[78,124],"connected":[79,89,125],"graph":[81,118],"(FC-WG).":[82],"In":[83],"this":[84],"case":[85],"each":[86],"every":[91],"other":[92],"point,":[93],"and":[94],"only":[95],"weights":[97],"control":[98],"outcome":[100],"Another":[104],"uses":[106],"proposed":[108],"graph,":[109,126],"so":[110],"called":[111],"nearest":[113],"points":[114],"(NPP-WG),":[119],"which":[120],"not":[122],"because":[127],"connections":[129],"are":[132,152],"restricted.":[133],"investigated":[135],"our":[136],"theoretical":[137],"datasets.":[142],"results":[144],"show":[145],"clustering":[148],"graphs":[151],"suitable":[153],"points,":[157],"while":[158],"it":[159],"also":[160],"provides":[161],"grouping":[163],"point-sets.":[165]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}