{"id":"https://openalex.org/W2152354418","doi":"https://doi.org/10.1109/icassp.2012.6288281","title":"A mode-based clustering algorithm without mode seeking","display_name":"A mode-based clustering algorithm without mode seeking","publication_year":2012,"publication_date":"2012-03-01","ids":{"openalex":"https://openalex.org/W2152354418","doi":"https://doi.org/10.1109/icassp.2012.6288281","mag":"2152354418"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2012.6288281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5070055975","display_name":"Esra Ataer-Cans\u0131zo\u011flu","orcid":"https://orcid.org/0000-0002-1941-4241"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Esra Ataer-Cansizoglu","raw_affiliation_strings":["Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083261801","display_name":"Deniz Erdo\u011fmu\u015f","orcid":"https://orcid.org/0000-0002-1114-3539"},"institutions":[{"id":"https://openalex.org/I12912129","display_name":"Northeastern University","ror":"https://ror.org/04t5xt781","country_code":"US","type":"education","lineage":["https://openalex.org/I12912129"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Deniz Erdogmus","raw_affiliation_strings":["Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA"],"affiliations":[{"raw_affiliation_string":"Cognitive Systems Laboratory, Northeastern University, Boston, MA, USA","institution_ids":["https://openalex.org/I12912129"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5070055975"],"corresponding_institution_ids":["https://openalex.org/I12912129"],"apc_list":null,"apc_paid":null,"fwci":0.4281,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73465612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3952","issue":null,"first_page":"1925","last_page":"1928"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9987000226974487,"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":0.9987000226974487,"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":0.9914000034332275,"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/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.9699000120162964,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7742146253585815},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5956186652183533},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.4650266170501709},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4647136926651001},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4564618170261383},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4295962154865265},{"id":"https://openalex.org/keywords/hill-climbing","display_name":"Hill climbing","score":0.4205359220504761},{"id":"https://openalex.org/keywords/correlation-clustering","display_name":"Correlation clustering","score":0.4178483188152313},{"id":"https://openalex.org/keywords/minor","display_name":"Minor (academic)","score":0.4172728955745697},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.41395947337150574},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.38315409421920776},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.35930007696151733},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3073708415031433},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2634581923484802}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7742146253585815},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5956186652183533},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.4650266170501709},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4647136926651001},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4564618170261383},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4295962154865265},{"id":"https://openalex.org/C135450995","wikidata":"https://www.wikidata.org/wiki/Q820272","display_name":"Hill climbing","level":2,"score":0.4205359220504761},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.4178483188152313},{"id":"https://openalex.org/C2779760435","wikidata":"https://www.wikidata.org/wiki/Q5396169","display_name":"Minor (academic)","level":2,"score":0.4172728955745697},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.41395947337150574},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38315409421920776},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.35930007696151733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3073708415031433},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2634581923484802},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2012.6288281","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2012.6288281","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1488789879","https://openalex.org/W1508404128","https://openalex.org/W1964806616","https://openalex.org/W2022686119","https://openalex.org/W2096496077","https://openalex.org/W2106011042","https://openalex.org/W2143555583","https://openalex.org/W2153233077","https://openalex.org/W2158819362","https://openalex.org/W2164500538","https://openalex.org/W3100742177","https://openalex.org/W6674723449"],"related_works":["https://openalex.org/W1981213098","https://openalex.org/W2367205823","https://openalex.org/W2384052049","https://openalex.org/W4231226332","https://openalex.org/W2087424554","https://openalex.org/W4200113299","https://openalex.org/W2379907417","https://openalex.org/W4220814143","https://openalex.org/W2161927371","https://openalex.org/W2769245605"],"abstract_inverted_index":{"Mode-based":[0],"clustering":[1,54,121],"approaches":[2],"such":[3],"as":[4,39],"mean-shift":[5],"and":[6,143],"its":[7],"variants":[8],"are":[9,13,133],"extremely":[10],"successful.":[11],"They":[12],"also":[14],"computationally":[15],"expensive":[16],"due":[17],"to":[18,97],"their":[19],"iterative":[20,60],"hill-climbing":[21],"strategy":[22],"when":[23,92],"determining":[24],"cluster":[25,35],"labels":[26],"for":[27,63,73],"samples.":[28,87],"We":[29],"identify":[30],"the":[31,43,74,124],"fact":[32],"that":[33,56,83],"mode-based":[34,53],"boundaries":[36],"exhibit":[37],"themselves":[38],"minor":[40,78],"surfaces":[41],"of":[42,76,86],"data":[44,90],"distribution.":[45],"Based":[46],"on":[47,71,80,104,136],"this":[48,129],"observation,":[49],"we":[50],"develop":[51],"a":[52,77,81,98],"methodology":[55],"does":[57],"not":[58],"involve":[59],"hill":[61],"climbing":[62],"each":[64],"sample.":[65],"The":[66,88],"method,":[67],"instead,":[68],"is":[69,116],"based":[70,103],"searching":[72],"presence":[75],"surface":[79],"path":[82],"connects":[84],"pairs":[85],"pairwise":[89],"connections,":[91],"evaluated":[93],"efficiently,":[94],"may":[95],"lead":[96],"simple":[99],"graph":[100],"connectivity":[101],"matrix":[102],"which":[105],"clusters":[106],"can":[107],"be":[108],"identified":[109],"using":[110,139],"connected":[111],"components.":[112],"This":[113],"search":[114],"efficiency":[115],"achieved":[117],"by":[118],"an":[119],"agglomerative":[120],"approach":[122],"in":[123,128],"particular":[125],"proposition":[126],"presented":[127],"paper.":[130],"Illustrative":[131],"experiments":[132],"carried":[134],"out":[135],"synthetic":[137],"datasets":[138],"Gaussian":[140],"mixture":[141],"models":[142],"kernel":[144],"density":[145],"estimates.":[146]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
