{"id":"https://openalex.org/W6929587286","doi":"https://doi.org/10.48550/arxiv.2503.22448","title":"Comparison between neural network clustering, hierarchical clustering and k-means clustering: Applications using fluidic lenses","display_name":"Comparison between neural network clustering, hierarchical clustering and k-means clustering: Applications using fluidic lenses","publication_year":2025,"publication_date":"2025-03-28","ids":{"openalex":"https://openalex.org/W6929587286","doi":"https://doi.org/10.48550/arxiv.2503.22448"},"language":"en","primary_location":{"id":"pmh:oai:figshare.com:article/28707023","is_oa":true,"landing_page_url":"https://figshare.com/articles/preprint/Comparison_between_neural_network_clustering_hierarchical_clustering_and_k-means_clustering_Applications_using_fluidic_lenses/28707023","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://figshare.com/articles/preprint/Comparison_between_neural_network_clustering_hierarchical_clustering_and_k-means_clustering_Applications_using_fluidic_lenses/28707023","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Puentes, Graciana","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Puentes, Graciana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T10066","display_name":"Gut microbiota and health","score":0.250900000333786,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10066","display_name":"Gut microbiota and health","score":0.250900000333786,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11791","display_name":"Microbial Community Ecology and Physiology","score":0.07000000029802322,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10428","display_name":"Reproductive tract infections research","score":0.04560000076889992,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7627999782562256},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6640999913215637},{"id":"https://openalex.org/keywords/zernike-polynomials","display_name":"Zernike polynomials","score":0.6065999865531921},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5604000091552734},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.5196999907493591},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4408999979496002},{"id":"https://openalex.org/keywords/single-linkage-clustering","display_name":"Single-linkage clustering","score":0.4375999867916107},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4268999993801117},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.39640000462532043}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7627999782562256},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6640999913215637},{"id":"https://openalex.org/C92423082","wikidata":"https://www.wikidata.org/wiki/Q132146","display_name":"Zernike polynomials","level":3,"score":0.6065999865531921},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5947999954223633},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5604000091552734},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.5196999907493591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49900001287460327},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4408999979496002},{"id":"https://openalex.org/C22648726","wikidata":"https://www.wikidata.org/wiki/Q7523744","display_name":"Single-linkage clustering","level":5,"score":0.4375999867916107},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4268999993801117},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.39640000462532043},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3946000039577484},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34529998898506165},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3434999883174896},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.3246999979019165},{"id":"https://openalex.org/C165699331","wikidata":"https://www.wikidata.org/wiki/Q461533","display_name":"Wavefront","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.25940001010894775},{"id":"https://openalex.org/C23822008","wikidata":"https://www.wikidata.org/wiki/Q5156437","display_name":"Complete-linkage clustering","level":5,"score":0.25429999828338623},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C2780985081","wikidata":"https://www.wikidata.org/wiki/Q676502","display_name":"Data Matrix","level":5,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:figshare.com:article/28707023","is_oa":true,"landing_page_url":"https://figshare.com/articles/preprint/Comparison_between_neural_network_clustering_hierarchical_clustering_and_k-means_clustering_Applications_using_fluidic_lenses/28707023","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},{"id":"doi:10.48550/arxiv.2503.22448","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2503.22448","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/28707023","is_oa":true,"landing_page_url":"https://figshare.com/articles/preprint/Comparison_between_neural_network_clustering_hierarchical_clustering_and_k-means_clustering_Applications_using_fluidic_lenses/28707023","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"comparison":[1],"between":[2],"neural":[3],"network":[4],"clustering":[5,8,12,175,237,259],"(NNC),":[6],"hierarchical":[7],"(HC)":[9],"and":[10,75,93,226],"K-means":[11],"(KMC)":[13],"is":[14,233],"performed":[15],"to":[16,43,71,98,169,256],"evaluate":[17],"the":[18,58,62,73,78,104,109,113,193,196,201,208,218,223,248],"computational":[19],"superiority":[20],"of":[21,46,53,61,77,103,111,125,142,151,156,174,183,211,220,229],"these":[22],"three":[23],"machine":[24],"learning":[25],"(ML)":[26],"techniques":[27],"for":[28,159,185],"organizing":[29],"large":[30],"datasets":[31],"into":[32,188],"clusters.":[33,191,213],"For":[34],"NNC,":[35],"a":[36,44,100,118,131,139,154,164,171,179,234],"self-organizing":[37],"map":[38],"(SOM)":[39],"training":[40],"was":[41,115,128,144,167,215],"applied":[42],"collection":[45],"wavefront":[47],"sensor":[48],"reconstructions,":[49],"decomposed":[50],"in":[51],"terms":[52],"15":[54,79,202],"Zernike":[55,80],"coefficients,":[56],"characterizing":[57],"optical":[59],"aberrations":[60],"phase":[63],"front":[64],"transmitted":[65],"by":[66,130,146],"fluidic":[67],"lenses.":[68],"In":[69,108,162],"order":[70],"understand":[72],"distribution":[74],"structure":[76],"variables":[81,203],"within":[82],"an":[83,148],"input":[84],"space,":[85],"SOM-neighboring":[86],"weight":[87],"distances,":[88],"SOM-sample":[89],"hits,":[90],"SOM-weight":[91,94],"positions":[92],"planes":[95],"were":[96],"analyzed":[97],"form":[99],"visual":[101],"interpretation":[102],"system's":[105],"structural":[106],"properties.":[107],"case":[110],"HC,":[112,245],"data":[114,186],"partitioned":[116],"using":[117],"combined":[119,224],"dissimilarity-linkage":[120],"matrix":[121],"computation.":[122],"The":[123],"effectiveness":[124],"this":[126],"method":[127],"confirmed":[129],"high":[132],"cophenetic":[133],"correlation":[134],"coefficient":[135],"value":[136,182],"(c=0.9651).":[137],"Additionally,":[138],"maximum":[140],"number":[141],"clusters":[143,158,221],"established":[145,216],"setting":[147],"inconsistency":[149,252],"cutoff":[150,253],"0.8,":[152],"yielding":[153],"total":[155],"7":[157],"system":[160],"segmentation.":[161],"addition,":[163],"KMC":[165,232],"approach":[166],"employed":[168],"establish":[170],"quantitative":[172],"measure":[173],"segmentation":[176,187],"efficiency,":[177],"obtaining":[178],"sillhoute":[180],"average":[181],"0.905":[184],"K=5":[189],"non-overlapping":[190],"On":[192],"other":[194],"hand,":[195],"NNC":[197,243],"analysis":[198],"revealed":[199],"that":[200,217],"could":[204],"be":[205],"characterized":[206],"through":[207,222],"collective":[209],"influence":[210],"8":[212],"It":[214],"formation":[219],"linkage":[225],"dissimilarity":[227],"algorithms":[228],"HC":[230],"alongside":[231],"more":[235],"dependable":[236],"solution":[238],"than":[239],"separate":[240],"assessment":[241],"via":[242],"or":[244,251],"where":[246],"altering":[247],"SOM":[249],"size":[250],"can":[254],"lead":[255],"completely":[257],"new":[258],"configurations.":[260]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}