{"id":"https://openalex.org/W4413823433","doi":"https://doi.org/10.1109/icccn65249.2025.11133860","title":"Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data","display_name":"Quantum Feature Optimization for Enhanced Clustering of Blockchain Transaction Data","publication_year":2025,"publication_date":"2025-08-04","ids":{"openalex":"https://openalex.org/W4413823433","doi":"https://doi.org/10.1109/icccn65249.2025.11133860"},"language":"en","primary_location":{"id":"doi:10.1109/icccn65249.2025.11133860","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn65249.2025.11133860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 34th International Conference on Computer Communications and Networks (ICCCN)","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/A5008816013","display_name":"Yun\u2010Cheng Tsai","orcid":"https://orcid.org/0000-0002-6266-7260"},"institutions":[{"id":"https://openalex.org/I134161618","display_name":"National Taiwan Normal University","ror":"https://ror.org/059dkdx38","country_code":"TW","type":"education","lineage":["https://openalex.org/I134161618"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Yun-Cheng Tsai","raw_affiliation_strings":["National Taiwan Normal University"],"affiliations":[{"raw_affiliation_string":"National Taiwan Normal University","institution_ids":["https://openalex.org/I134161618"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021414038","display_name":"Samuel Yen-Chi Chen","orcid":"https://orcid.org/0000-0003-0114-4826"},"institutions":[{"id":"https://openalex.org/I166794780","display_name":"Wells Fargo (United States)","ror":"https://ror.org/037r2ff59","country_code":"US","type":"company","lineage":["https://openalex.org/I166794780"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Samuel Yen-Chi Chen","raw_affiliation_strings":["Wells Fargo"],"affiliations":[{"raw_affiliation_string":"Wells Fargo","institution_ids":["https://openalex.org/I166794780"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5008816013"],"corresponding_institution_ids":["https://openalex.org/I134161618"],"apc_list":null,"apc_paid":null,"fwci":3.4721,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.94134997,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9642000198364258,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10270","display_name":"Blockchain Technology Applications and Security","score":0.9642000198364258,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/blockchain","display_name":"Blockchain","score":0.9150493144989014},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7118565440177917},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.6939812898635864},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6755852699279785},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5828996300697327},{"id":"https://openalex.org/keywords/quantum","display_name":"Quantum","score":0.5220351219177246},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.42476916313171387},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.22756889462471008},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.18903395533561707},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.15050506591796875},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.07578766345977783}],"concepts":[{"id":"https://openalex.org/C2779687700","wikidata":"https://www.wikidata.org/wiki/Q20514253","display_name":"Blockchain","level":2,"score":0.9150493144989014},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7118565440177917},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.6939812898635864},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6755852699279785},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5828996300697327},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.5220351219177246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42476916313171387},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.22756889462471008},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.18903395533561707},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.15050506591796875},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.07578766345977783},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccn65249.2025.11133860","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccn65249.2025.11133860","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 34th International Conference on Computer Communications and Networks (ICCCN)","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":18,"referenced_works":["https://openalex.org/W2015811642","https://openalex.org/W2153233077","https://openalex.org/W2521267242","https://openalex.org/W2559394418","https://openalex.org/W2781738013","https://openalex.org/W2790388700","https://openalex.org/W2798434869","https://openalex.org/W2803086154","https://openalex.org/W2980446414","https://openalex.org/W3111162498","https://openalex.org/W4406261916","https://openalex.org/W4406262067","https://openalex.org/W4406355331","https://openalex.org/W4408355722","https://openalex.org/W4410394156","https://openalex.org/W4410609086","https://openalex.org/W4410771607","https://openalex.org/W4412956029"],"related_works":["https://openalex.org/W4210406818","https://openalex.org/W4306779889","https://openalex.org/W3048554917","https://openalex.org/W3211706803","https://openalex.org/W4382775358","https://openalex.org/W4246942721","https://openalex.org/W3209862047","https://openalex.org/W3126939372","https://openalex.org/W4386732777","https://openalex.org/W4304136894"],"abstract_inverted_index":{"Blockchain":[0],"transaction":[1],"data":[2],"exhibits":[3],"high":[4],"dimensionality,":[5],"noise,":[6],"and":[7,59,98],"intricate":[8],"feature":[9,37,81],"entanglement,":[10],"presenting":[11],"significant":[12],"challenges":[13],"for":[14,83],"traditional":[15],"clustering":[16,28,84,118],"algorithms.":[17],"In":[18],"this":[19],"study,":[20],"we":[21],"conduct":[22],"a":[23,65,70,74],"comparative":[24],"analysis":[25],"of":[26,94,101],"three":[27],"approaches:":[29],"(1)":[30],"Classical":[31],"K-Means":[32],"Clustering,":[33,41,63],"applied":[34],"to":[35,78],"pre-processed":[36],"representations;":[38],"(2)":[39],"Hybrid":[40],"wherein":[42],"classical":[43],"features":[44,50],"are":[45],"enhanced":[46],"with":[47],"quantum":[48,55,95,108],"random":[49],"extracted":[51],"using":[52],"randomly":[53],"initialized":[54],"neural":[56],"networks":[57],"(QNNs);":[58],"(3)":[60],"Fully":[61],"Quantum":[62],"where":[64],"QNN":[66],"is":[67],"trained":[68],"in":[69],"self-supervised":[71],"manner":[72],"leveraging":[73],"SwAV-based":[75],"loss":[76],"function":[77],"optimize":[79],"the":[80,92,99],"space":[82],"directly.":[85],"The":[86],"proposed":[87],"experimental":[88],"framework":[89],"systematically":[90],"investigates":[91],"impact":[93],"circuit":[96],"depth":[97],"number":[100],"learned":[102],"prototypes,":[103],"demonstrating":[104],"that":[105],"even":[106],"shallow":[107],"circuits":[109],"can":[110],"effectively":[111],"extract":[112],"meaningful":[113],"non-linear":[114],"representations,":[115],"significantly":[116],"improving":[117],"performance.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}