{"id":"https://openalex.org/W2011009831","doi":"https://doi.org/10.1109/bibe.2007.4375659","title":"Bottom-Up Multiple Row Addition Algorithms for the Biclustering-Problem","display_name":"Bottom-Up Multiple Row Addition Algorithms for the Biclustering-Problem","publication_year":2007,"publication_date":"2007-10-01","ids":{"openalex":"https://openalex.org/W2011009831","doi":"https://doi.org/10.1109/bibe.2007.4375659","mag":"2011009831"},"language":"en","primary_location":{"id":"doi:10.1109/bibe.2007.4375659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe.2007.4375659","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE 7th International Symposium on BioInformatics and BioEngineering","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/A5072822414","display_name":"Hyung-Won Koh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121365","display_name":"Leibniz Institute for Analytical Sciences - ISAS","ror":"https://ror.org/02jhqqg57","country_code":"DE","type":"facility","lineage":["https://openalex.org/I315704651","https://openalex.org/I4210121365"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Hyung-Won Koh","raw_affiliation_strings":["ISAS-Institute for Analytical Sciences, Dortmund, Germany","Inst. for Anal. Sci., Dortmund"],"affiliations":[{"raw_affiliation_string":"ISAS-Institute for Analytical Sciences, Dortmund, Germany","institution_ids":["https://openalex.org/I4210121365"]},{"raw_affiliation_string":"Inst. for Anal. Sci., Dortmund","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041767794","display_name":"Lars Hildebrand","orcid":null},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Lars Hildebrand","raw_affiliation_strings":["University of Dortmund, Dortmund, Germany","University of Dortmund, 44221 Dortmund, Germany. Email: lars.hildebrand@udo.edu"],"affiliations":[{"raw_affiliation_string":"University of Dortmund, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]},{"raw_affiliation_string":"University of Dortmund, 44221 Dortmund, Germany. Email: lars.hildebrand@udo.edu","institution_ids":["https://openalex.org/I200332995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5072822414"],"corresponding_institution_ids":["https://openalex.org/I4210121365"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.07688957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"849","last_page":"856"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9958999752998352,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9958999752998352,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9945999979972839,"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/T10885","display_name":"Gene expression and cancer classification","score":0.9933000206947327,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.7824588418006897},{"id":"https://openalex.org/keywords/biclustering","display_name":"Biclustering","score":0.6818900108337402},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6121369004249573},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.5428772568702698},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5130428075790405},{"id":"https://openalex.org/keywords/row-and-column-spaces","display_name":"Row and column spaces","score":0.4715307354927063},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3891996145248413},{"id":"https://openalex.org/keywords/row","display_name":"Row","score":0.3766699433326721},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3268973231315613},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.2785240411758423},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.23714002966880798}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.7824588418006897},{"id":"https://openalex.org/C144817290","wikidata":"https://www.wikidata.org/wiki/Q2976575","display_name":"Biclustering","level":5,"score":0.6818900108337402},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6121369004249573},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.5428772568702698},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5130428075790405},{"id":"https://openalex.org/C104140500","wikidata":"https://www.wikidata.org/wiki/Q2088159","display_name":"Row and column spaces","level":3,"score":0.4715307354927063},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3891996145248413},{"id":"https://openalex.org/C135598885","wikidata":"https://www.wikidata.org/wiki/Q1366302","display_name":"Row","level":2,"score":0.3766699433326721},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3268973231315613},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.2785240411758423},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.23714002966880798},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C33704608","wikidata":"https://www.wikidata.org/wiki/Q5014717","display_name":"CURE data clustering algorithm","level":4,"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/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C94641424","wikidata":"https://www.wikidata.org/wiki/Q5172845","display_name":"Correlation clustering","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibe.2007.4375659","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibe.2007.4375659","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 IEEE 7th International Symposium on BioInformatics and BioEngineering","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.47999998927116394,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334763","display_name":"Leibniz-Gemeinschaft","ror":"https://ror.org/01n6r0e97"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W196542726","https://openalex.org/W1493217831","https://openalex.org/W1874456553","https://openalex.org/W2105883975","https://openalex.org/W2108025037","https://openalex.org/W2114417150","https://openalex.org/W2115875363","https://openalex.org/W2119282901","https://openalex.org/W2125138557","https://openalex.org/W2136107412","https://openalex.org/W2137683543","https://openalex.org/W2144544802","https://openalex.org/W2147246240","https://openalex.org/W2161732139","https://openalex.org/W2295428206","https://openalex.org/W4231824804","https://openalex.org/W4231916799","https://openalex.org/W6887916226"],"related_works":["https://openalex.org/W2313440505","https://openalex.org/W3123744736","https://openalex.org/W2137691148","https://openalex.org/W1505848319","https://openalex.org/W2152668278","https://openalex.org/W3084408672","https://openalex.org/W1979810043","https://openalex.org/W2397510698","https://openalex.org/W2506426934","https://openalex.org/W2611629964"],"abstract_inverted_index":{"The":[0,28,105],"discovery":[1],"of":[2,30,35,74,113,116,124],"locally":[3],"and":[4,17,95,120],"significantly":[5],"correlated":[6],"subpatterns":[7],"within":[8,39],"a":[9,31,40,43,71,85,117],"two-dimensional":[10],"dataset":[11],"has":[12],"recently":[13],"become":[14],"quite":[15],"popular":[16,118],"is":[18,53,107],"amongst":[19],"others":[20],"addressed":[21],"by":[22,110,121],"methods":[23],"solving":[24],"the":[25,47,56,68,79,91,111,122],"biclustering":[26],"problem.":[27],"preservation":[29],"particularly":[32],"defined":[33],"degree":[34],"homogeneity":[36],"between":[37],"elements":[38],"bicluster":[41,114],"plays":[42],"key":[44],"role":[45],"in":[46],"search":[48,80],"procedure.":[49,81],"A":[50],"prominent":[51],"quantity":[52],"known":[54],"as":[55],"mean":[57,92],"squared":[58,93],"residue.":[59],"Most":[60],"approaches":[61],"use":[62],"such":[63],"measures":[64],"only":[65],"to":[66,90],"evaluate":[67],"found":[69],"solutions":[70],"posteriori,":[72],"instead":[73],"incorporating":[75],"them":[76],"directly":[77],"into":[78,99],"This":[82],"work":[83],"proposes":[84],"pairwise":[86],"distance":[87],"function":[88],"related":[89],"residue":[94],"includes":[96],"this":[97],"measure":[98],"two":[100],"new":[101],"multiple":[102],"enrichment":[103,112,123],"algorithms.":[104],"impact":[106],"demonstrated":[108],"empirically":[109],"sets":[115],"method":[119],"randomly":[125],"chosen":[126],"single":[127],"rows.":[128]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}