{"id":"https://openalex.org/W7123342726","doi":"https://doi.org/10.1109/access.2026.3651734","title":"An Adaptive Hybrid Clustering Framework With Iterative Noise Filtering and a Novel DBNS Ratio Validity Measure for Outlier-Resilient High-Dimensional Data","display_name":"An Adaptive Hybrid Clustering Framework With Iterative Noise Filtering and a Novel DBNS Ratio Validity Measure for Outlier-Resilient High-Dimensional Data","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7123342726","doi":"https://doi.org/10.1109/access.2026.3651734"},"language":null,"primary_location":{"id":"doi:10.1109/access.2026.3651734","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651734","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2026.3651734","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122859570","display_name":"Abhishek","orcid":null},"institutions":[{"id":"https://openalex.org/I115715567","display_name":"Birla Institute of Technology, Mesra","ror":"https://ror.org/028vtqb15","country_code":"IN","type":"education","lineage":["https://openalex.org/I115715567"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Abhishek","raw_affiliation_strings":["Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India","institution_ids":["https://openalex.org/I115715567"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063958309","display_name":"Partha Sarathi Bishnu","orcid":"https://orcid.org/0000-0001-5143-6195"},"institutions":[{"id":"https://openalex.org/I115715567","display_name":"Birla Institute of Technology, Mesra","ror":"https://ror.org/028vtqb15","country_code":"IN","type":"education","lineage":["https://openalex.org/I115715567"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Partha Sarathi Bishnu","raw_affiliation_strings":["Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India","institution_ids":["https://openalex.org/I115715567"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122853749","display_name":"Vandana Bhattacharjee","orcid":null},"institutions":[{"id":"https://openalex.org/I115715567","display_name":"Birla Institute of Technology, Mesra","ror":"https://ror.org/028vtqb15","country_code":"IN","type":"education","lineage":["https://openalex.org/I115715567"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Vandana Bhattacharjee","raw_affiliation_strings":["Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Birla Institute of Technology, Ranchi, Jharkhand, India","institution_ids":["https://openalex.org/I115715567"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5122859570"],"corresponding_institution_ids":["https://openalex.org/I115715567"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08550154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":null,"first_page":"6167","last_page":"6185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6628999710083008,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6628999710083008,"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":0.2615000009536743,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.016899999231100082,"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":0.7325999736785889},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6287000179290771},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6062999963760376},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5478000044822693},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.5342000126838684},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5133000016212463},{"id":"https://openalex.org/keywords/cure-data-clustering-algorithm","display_name":"CURE data clustering algorithm","score":0.4422000050544739},{"id":"https://openalex.org/keywords/silhouette","display_name":"Silhouette","score":0.3950999975204468}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7325999736785889},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7077000141143799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6287000179290771},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6186000108718872},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6062999963760376},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5133000016212463},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"score":0.4422000050544739},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42260000109672546},{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C104047586","wikidata":"https://www.wikidata.org/wiki/Q5033439","display_name":"Canopy clustering algorithm","level":4,"score":0.3718000054359436},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.3716000020503998},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3440999984741211},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.3416999876499176},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.3409999907016754},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.3262999951839447},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/access.2026.3651734","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651734","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1109/access.2026.3651734","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2026.3651734","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G111939725","display_name":null,"funder_award_id":"SG-12/2024 (E-Office 220800)","funder_id":"https://openalex.org/F4320320720","funder_display_name":"Indian Council of Medical Research"},{"id":"https://openalex.org/G1624771669","display_name":null,"funder_award_id":"Dev/SG-12/2024 (E-Office 220800)","funder_id":"https://openalex.org/F4320320720","funder_display_name":"Indian Council of Medical Research"}],"funders":[{"id":"https://openalex.org/F4320320720","display_name":"Indian Council of Medical Research","ror":"https://ror.org/0492wrx28"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2011430131","https://openalex.org/W2051224630","https://openalex.org/W2100495367","https://openalex.org/W2145793758","https://openalex.org/W2784378868","https://openalex.org/W2913332651","https://openalex.org/W2914095169","https://openalex.org/W2914740624","https://openalex.org/W2964346923","https://openalex.org/W2990722563","https://openalex.org/W2994497769","https://openalex.org/W3003753408","https://openalex.org/W3082129493","https://openalex.org/W3114057780","https://openalex.org/W3139506867","https://openalex.org/W3164952570","https://openalex.org/W3180794169","https://openalex.org/W3208978091","https://openalex.org/W3210000287","https://openalex.org/W3211298820","https://openalex.org/W4211230421","https://openalex.org/W4213375498","https://openalex.org/W4225812168","https://openalex.org/W4235169531","https://openalex.org/W4281885333","https://openalex.org/W4290830108","https://openalex.org/W4293242646","https://openalex.org/W4294865396","https://openalex.org/W4299627282","https://openalex.org/W4306664511","https://openalex.org/W4311168011","https://openalex.org/W4379618852","https://openalex.org/W4390877308","https://openalex.org/W4393405326","https://openalex.org/W4412352698"],"related_works":[],"abstract_inverted_index":{"Clustering":[0],"high-dimensional":[1],"data":[2,50,77],"while":[3],"assuring":[4],"robustness":[5,129],"to":[6,38,47],"noise":[7,36],"and":[8,34,95,116,123,130],"outliers":[9],"remains":[10],"a":[11,52],"significant":[12],"challenge":[13],"in":[14,30,141],"unsupervised":[15],"learning.":[16],"This":[17,105],"paper":[18],"proposes":[19],"an":[20,45,92],"adaptive":[21,68],"hybrid":[22,83],"clustering":[23,57,109,139],"framework":[24,43],"that":[25,88],"integrates":[26,89],"the":[27,31,61,73,96,127,133,137],"Mahalanobis":[28,62],"distance":[29,63],"latent":[32,54],"space":[33],"iterative":[35],"filtering":[37],"enhance":[39],"outlier":[40,65,93],"resilience.":[41],"The":[42],"employs":[44],"autoencoder":[46],"transform":[48],"raw":[49],"into":[51],"compact":[53],"representation,":[55],"where":[56],"is":[58,86],"performed":[59],"using":[60],"for":[64],"detection.":[66],"An":[67],"re-weighting":[69],"mechanism":[70],"iteratively":[71],"minimizes":[72],"impact":[74],"of":[75,132],"noisy":[76,142],"points,":[78],"refining":[79],"cluster":[80],"assignments.":[81],"A":[82],"loss":[84,107],"function":[85],"proposed":[87,134],"reconstruction":[90],"accuracy,":[91],"penalty,":[94],"novel":[97],"Davies-Bouldin":[98],"Index":[99],"Normalized":[100],"Silhouette":[101],"Score":[102],"(DBNS)":[103],"ratio.":[104],"combined":[106],"optimizes":[108],"quality":[110],"by":[111],"jointly":[112],"enhancing":[113],"intra-cluster":[114],"cohesion":[115],"inter-cluster":[117],"separation.":[118],"Experimental":[119],"results":[120],"on":[121],"synthetic":[122],"real":[124],"datasets":[125],"demonstrate":[126],"superior":[128],"precision":[131],"approach,":[135],"outperforming":[136],"latest":[138],"methods":[140],"environments.":[143]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2026-01-14T00:00:00"}
