{"id":"https://openalex.org/W2610001991","doi":"https://doi.org/10.1109/icpr.2016.7900198","title":"MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data","display_name":"MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2610001991","doi":"https://doi.org/10.1109/icpr.2016.7900198","mag":"2610001991"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7900198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/conference_contribution/MCNC_multi-channel_nonparametric_clustering_from_heterogeneous_data/20850895","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100613698","display_name":"Thanh-Binh Nguyen","orcid":"https://orcid.org/0000-0001-6268-7408"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Thanh-Binh Nguyen","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics, Deakin University, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics, Deakin University, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101775198","display_name":"Vu Nguyen","orcid":"https://orcid.org/0000-0002-0294-4561"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Vu Nguyen","raw_affiliation_strings":["Deakin University, Burwood, VIC, AU"],"affiliations":[{"raw_affiliation_string":"Deakin University, Burwood, VIC, AU","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045540854","display_name":"Svetha Venkatesh","orcid":"https://orcid.org/0000-0001-8675-6631"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Svetha Venkatesh","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics, Deakin University, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics, Deakin University, Australia","institution_ids":["https://openalex.org/I149704539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036447132","display_name":"Dinh Phung","orcid":"https://orcid.org/0000-0002-9977-8247"},"institutions":[{"id":"https://openalex.org/I149704539","display_name":"Deakin University","ror":"https://ror.org/02czsnj07","country_code":"AU","type":"education","lineage":["https://openalex.org/I149704539"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Dinh Phung","raw_affiliation_strings":["Centre for Pattern Recognition and Data Analytics, Deakin University, Australia"],"affiliations":[{"raw_affiliation_string":"Centre for Pattern Recognition and Data Analytics, Deakin University, Australia","institution_ids":["https://openalex.org/I149704539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100613698"],"corresponding_institution_ids":["https://openalex.org/I149704539"],"apc_list":null,"apc_paid":null,"fwci":1.3302,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.88437212,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"4","issue":null,"first_page":"3633","last_page":"3638"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11106","display_name":"Data Management and Algorithms","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8046070337295532},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6554680466651917},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6258484125137329},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5614381432533264},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5577199459075928},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.53146892786026},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.5156096816062927},{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.4952785074710846},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.49324625730514526},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.46002286672592163},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4444442093372345},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3681252598762512},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.36720049381256104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8046070337295532},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6554680466651917},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6258484125137329},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5614381432533264},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5577199459075928},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.53146892786026},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.5156096816062927},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.4952785074710846},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.49324625730514526},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.46002286672592163},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4444442093372345},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3681252598762512},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36720049381256104},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icpr.2016.7900198","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7900198","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:dro.deakin.edu.au:DU:30096921","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402457","display_name":"Deakin Research Online (Deakin University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149704539","host_organization_name":"Deakin University","host_organization_lineage":["https://openalex.org/I149704539"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Paper"},{"id":"pmh:oai:figshare.com:article/20850895","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/MCNC_multi-channel_nonparametric_clustering_from_heterogeneous_data/20850895","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/20850895","is_oa":true,"landing_page_url":"https://figshare.com/articles/conference_contribution/MCNC_multi-channel_nonparametric_clustering_from_heterogeneous_data/20850895","pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1137794452","https://openalex.org/W1967687583","https://openalex.org/W2033765726","https://openalex.org/W2069429561","https://openalex.org/W2091797506","https://openalex.org/W2117100923","https://openalex.org/W2127218421","https://openalex.org/W2143776582","https://openalex.org/W2148706548","https://openalex.org/W2149171634","https://openalex.org/W2151967501","https://openalex.org/W2156567116","https://openalex.org/W2185902968","https://openalex.org/W2296653931","https://openalex.org/W2340596134","https://openalex.org/W2502795791","https://openalex.org/W2561537916","https://openalex.org/W2994844380","https://openalex.org/W6678914141","https://openalex.org/W6681031197","https://openalex.org/W6682205209","https://openalex.org/W6682569104","https://openalex.org/W6724689175"],"related_works":["https://openalex.org/W4380150146","https://openalex.org/W3024870410","https://openalex.org/W2410652950","https://openalex.org/W4283773154","https://openalex.org/W3139174110","https://openalex.org/W4289597203","https://openalex.org/W2085630472","https://openalex.org/W4243114048","https://openalex.org/W1977098485","https://openalex.org/W4285201053"],"abstract_inverted_index":{"Bayesian":[0],"nonparametric":[1],"(BNP)":[2],"models":[3],"have":[4,21],"recently":[5],"become":[6],"popular":[7],"due":[8],"to":[9,53,77,112,134],"their":[10],"flexibility":[11],"in":[12,151],"identifying":[13],"the":[14,46,75,136,145],"unknown":[15],"number":[16,98],"of":[17,35,99,118,147,154],"clusters.":[18],"However,":[19],"they":[20],"difficulties":[22],"handling":[23],"heterogeneous":[24],"data":[25,56,87],"from":[26,45,81,101],"multiple":[27,82],"sources.":[28],"Existing":[29],"BNP":[30,69,120],"methods":[31],"either":[32],"treat":[33,90],"each":[34],"these":[36],"sources":[37,57],"independently":[38],"-":[39],"hence":[40],"do":[41],"not":[42],"get":[43],"benefits":[44],"correlating":[47],"information":[48],"between":[49],"them,":[50],"or":[51],"require":[52],"explicitly":[54],"specify":[55],"as":[58],"primary":[59],"and":[60,89,103,131,156],"context":[61],"channels.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66],"present":[67],"a":[68,96,114,119,123],"framework,":[70],"termed":[71],"MCNC,":[72],"which":[73],"has":[74],"ability":[76],"(1)":[78],"discover":[79,135],"co-patterns":[80],"sources;":[83],"(2)":[84],"explore":[85],"multi-channel":[86],"simultaneously":[88],"them":[91],"equally;":[92],"(3)":[93],"automatically":[94],"identify":[95],"suitable":[97],"patterns":[100],"data;":[102],"(4)":[104],"handle":[105],"missing":[106,157],"data.":[107,158],"The":[108,141],"key":[109],"idea":[110],"is":[111],"utilize":[113],"richer":[115],"base":[116],"measure":[117],"model":[121],"being":[122],"product-space.":[124],"We":[125],"demonstrate":[126],"our":[127,148],"framework":[128,150],"on":[129],"synthetic":[130],"real-world":[132],"datasets":[133],"identity-location-time":[137],"(a.k.a":[138],"who-where-when)":[139],"patterns.":[140],"experimental":[142],"results":[143],"highlight":[144],"effectiveness":[146],"MCNC":[149],"both":[152],"cases":[153],"complete":[155]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}