{"id":"https://openalex.org/W3195139324","doi":"https://doi.org/10.1108/ijicc-03-2021-0053","title":"CDEC: a constrained deep embedded clustering","display_name":"CDEC: a constrained deep embedded clustering","publication_year":2021,"publication_date":"2021-08-07","ids":{"openalex":"https://openalex.org/W3195139324","doi":"https://doi.org/10.1108/ijicc-03-2021-0053","mag":"3195139324"},"language":"en","primary_location":{"id":"doi:10.1108/ijicc-03-2021-0053","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-03-2021-0053","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-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/A5068709393","display_name":"Elham Amirizadeh","orcid":"https://orcid.org/0000-0002-5924-6967"},"institutions":[{"id":"https://openalex.org/I166459259","display_name":"Shiraz University","ror":"https://ror.org/028qtbk54","country_code":"IR","type":"education","lineage":["https://openalex.org/I166459259"]}],"countries":["IR"],"is_corresponding":true,"raw_author_name":"Elham Amirizadeh","raw_affiliation_strings":["Department of Computer Sciences and Engineering, Shiraz University, Shiraz, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Computer Sciences and Engineering, Shiraz University, Shiraz, Iran","institution_ids":["https://openalex.org/I166459259"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079098947","display_name":"Reza Boostani","orcid":"https://orcid.org/0000-0003-0055-4452"},"institutions":[{"id":"https://openalex.org/I166459259","display_name":"Shiraz University","ror":"https://ror.org/028qtbk54","country_code":"IR","type":"education","lineage":["https://openalex.org/I166459259"]}],"countries":["IR"],"is_corresponding":false,"raw_author_name":"Reza Boostani","raw_affiliation_strings":["Department of Computer Sciences and Engineering, Shiraz University, Shiraz, Iran"],"affiliations":[{"raw_affiliation_string":"Department of Computer Sciences and Engineering, Shiraz University, Shiraz, Iran","institution_ids":["https://openalex.org/I166459259"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5068709393"],"corresponding_institution_ids":["https://openalex.org/I166459259"],"apc_list":null,"apc_paid":null,"fwci":0.4844,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.65161928,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"14","issue":"4","first_page":"686","last_page":"701"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9995999932289124,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973000288009644,"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":0.9952999949455261,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8751516342163086},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8033710718154907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7844278812408447},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.650039792060852},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5500895977020264},{"id":"https://openalex.org/keywords/constrained-clustering","display_name":"Constrained clustering","score":0.5344580411911011},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5176947116851807},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47743234038352966},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43615034222602844},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.40741628408432007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37646061182022095},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.2993783950805664}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8751516342163086},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8033710718154907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7844278812408447},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.650039792060852},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5500895977020264},{"id":"https://openalex.org/C27964816","wikidata":"https://www.wikidata.org/wiki/Q5164359","display_name":"Constrained clustering","level":5,"score":0.5344580411911011},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5176947116851807},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47743234038352966},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43615034222602844},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.40741628408432007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37646061182022095},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.2993783950805664},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1108/ijicc-03-2021-0053","is_oa":false,"landing_page_url":"https://doi.org/10.1108/ijicc-03-2021-0053","pdf_url":null,"source":{"id":"https://openalex.org/S124503262","display_name":"International Journal of Intelligent Computing and Cybernetics","issn_l":"1756-378X","issn":["1756-378X","1756-3798"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319811","host_organization_name":"Emerald Publishing Limited","host_organization_lineage":["https://openalex.org/P4310319811"],"host_organization_lineage_names":["Emerald Publishing Limited"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Intelligent Computing and Cybernetics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W80917968","https://openalex.org/W1558962159","https://openalex.org/W1575935080","https://openalex.org/W1665214252","https://openalex.org/W1673310716","https://openalex.org/W1875842236","https://openalex.org/W1902027874","https://openalex.org/W1971022913","https://openalex.org/W1998582365","https://openalex.org/W2013912476","https://openalex.org/W2052478943","https://openalex.org/W2077276425","https://openalex.org/W2083681515","https://openalex.org/W2086724024","https://openalex.org/W2112796928","https://openalex.org/W2120438042","https://openalex.org/W2123585936","https://openalex.org/W2129793592","https://openalex.org/W2131828344","https://openalex.org/W2132914434","https://openalex.org/W2134370969","https://openalex.org/W2141125852","https://openalex.org/W2145094598","https://openalex.org/W2150102617","https://openalex.org/W2153579005","https://openalex.org/W2163605009","https://openalex.org/W2173649752","https://openalex.org/W2176950688","https://openalex.org/W2254039850","https://openalex.org/W2405933695","https://openalex.org/W2432567885","https://openalex.org/W2542517113","https://openalex.org/W2565286326","https://openalex.org/W2571440227","https://openalex.org/W2769333905","https://openalex.org/W2925853897","https://openalex.org/W2942611897","https://openalex.org/W2944912514","https://openalex.org/W2950094539","https://openalex.org/W2951980266","https://openalex.org/W2963804023","https://openalex.org/W2966787006","https://openalex.org/W3128121219","https://openalex.org/W3146242605"],"related_works":["https://openalex.org/W1538188876","https://openalex.org/W2586427049","https://openalex.org/W1491908038","https://openalex.org/W2732437347","https://openalex.org/W2280013922","https://openalex.org/W2566341957","https://openalex.org/W1511448059","https://openalex.org/W2163563073","https://openalex.org/W2970954390","https://openalex.org/W4225679450"],"abstract_inverted_index":{"Purpose":[0],"The":[1,171,236,374],"aim":[2],"of":[3,37,44,69,91,99,112,115,270,279,283,291,307,379],"this":[4,32,97,153,180,196,293],"study":[5],"is":[6,55,74,79,94,294,388,394],"to":[7,19,179,182,202,231,255,286,303,324,328],"propose":[8],"a":[9,66,159,169,219,322],"deep":[10,191],"neural":[11],"network":[12,46,217,339],"(DNN)":[13],"method":[14,167,334,375],"that":[15,30,75,299,364],"uses":[16,110,168,300],"side":[17,301,326],"information":[18,33,111,302,327],"improve":[20,304],"clustering":[21,38,137,148,161,185,193,248,271,284,298,308,336,349,369,382],"results":[22,274],"for":[23,106,149,162,350,358,399],"big":[24,150,163,313,351,380,400],"datasets;":[25,352],"also,":[26,353],"the":[27,35,42,45,76,125,155,166,174,184,209,215,227,233,243,256,276,287,295,305,319,329,332,354,370,377,391],"authors":[28,156,172],"show":[29],"applying":[31],"improves":[34,335,376],"performance":[36,61,186,265,306,337,378],"and":[39,81,119,165,177,187,212,249,260,263,338,368,386,396],"also":[40],"increase":[41],"speed":[43],"training":[47],"convergence.":[48],"Design/methodology/approach":[49],"In":[50,152,195],"data":[51,77,381,392],"mining,":[52],"semisupervised":[53,82,92],"learning":[54,83,93,100],"an":[56,198],"interesting":[57],"approach":[58],"because":[59,390],"good":[60],"can":[62],"be":[63],"achieved":[64],"with":[65,242,315,360],"small":[67],"subset":[68],"labeled":[70],"data;":[71],"one":[72],"reason":[73],"labeling":[78,393],"expensive,":[80],"does":[84,101],"not":[85,102],"need":[86],"all":[87],"labels.":[88],"One":[89],"type":[90,98],"constrained":[95,147,160,190,246,348],"clustering;":[96],"use":[103],"class":[104],"labels":[105,311],"clustering.":[107],"Instead,":[108],"it":[109,189,387],"some":[113],"pairs":[114],"instances":[116,121],"(side":[117],"information),":[118],"these":[120],"maybe":[122],"are":[123,372],"in":[124,131,208,229,268,281,312,356],"same":[126],"cluster":[127,232],"(must-link":[128],"[ML])":[129],"or":[130],"different":[132],"clusters":[133],"(cannot-link":[134],"[CL]).":[135],"Constrained":[136],"was":[138,200,253],"studied":[139],"extensively;":[140],"however,":[141],"little":[142],"works":[143,344],"have":[144,157,345],"focused":[145,346],"on":[146,347],"datasets.":[151,401],"paper,":[154],"presented":[158],"datasets,":[164,262],"DNN.":[170,330],"inject":[173,325],"constraints":[175,228],"(ML":[176],"CL)":[178],"DNN":[181],"promote":[183],"call":[188],"embedded":[192],"(CDEC).":[194],"manner,":[197],"autoencoder":[199,250],"implemented":[201],"elicit":[203],"informative":[204],"low":[205],"dimensional":[206],"features":[207,367],"latent":[210],"space":[211],"then":[213],"retrain":[214],"encoder":[216],"using":[218,310,384],"proposed":[220,237,333],"Kullback\u2013Leibler":[221],"divergence":[222],"objective":[223],"function,":[224],"which":[225],"captures":[226],"order":[230],"projected":[234],"samples.":[235],"CDEC":[238,280],"has":[239],"been":[240],"compared":[241],"adversarial":[244],"autoencoder,":[245],"1-spectral":[247],"+":[251],"k-means":[252],"applied":[254],"known":[257],"MNIST,":[258],"Reuters-10k":[259],"USPS":[261],"their":[264],"were":[266],"assessed":[267],"terms":[269,282],"accuracy.":[272],"Empirical":[273],"confirmed":[275],"statistical":[277],"superiority":[278],"accuracy":[285],"counterparts.":[288],"Findings":[289],"First":[290],"all,":[292],"first":[296],"DNN-constrained":[297],"without":[309,383],"datasets":[314],"high":[316],"dimension.":[317],"Second,":[318],"author":[320],"defined":[321],"formula":[323],"Third,":[331],"convergence":[340],"speed.":[341],"Originality/value":[342],"Little":[343],"studies":[355],"DNNs":[357],"clustering,":[359],"specific":[361],"loss":[362],"function":[363],"simultaneously":[365],"extract":[366],"data,":[371],"rare.":[373],"labels,":[385],"important":[389],"expensive":[395],"time-consuming,":[397],"especially":[398]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}