{"id":"https://openalex.org/W2159814723","doi":"https://doi.org/10.1145/1150402.1150484","title":"Efficient multidimensional data representations based on multiple correspondence analysis","display_name":"Efficient multidimensional data representations based on multiple correspondence analysis","publication_year":2006,"publication_date":"2006-08-20","ids":{"openalex":"https://openalex.org/W2159814723","doi":"https://doi.org/10.1145/1150402.1150484","mag":"2159814723"},"language":"en","primary_location":{"id":"doi:10.1145/1150402.1150484","doi":"https://doi.org/10.1145/1150402.1150484","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1150402.1150484","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","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":null,"display_name":"Riadh Ben Messaoud","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I188626449","display_name":"Universit\u00e9 Lumi\u00e8re Lyon 2","ror":"https://ror.org/03rth4p18","country_code":"FR","type":"education","lineage":["https://openalex.org/I188626449","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Riadh Ben Messaoud","raw_affiliation_strings":["University of Lyon 2, Bron Cedex, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Lyon 2, Bron Cedex, France","institution_ids":["https://openalex.org/I188626449"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080801111","display_name":"Omar Boussa\u00efd","orcid":"https://orcid.org/0000-0001-6388-3152","observed_orcids":["https://orcid.org/0000-0001-6388-3152"]},"institutions":[{"id":"https://openalex.org/I188626449","display_name":"Universit\u00e9 Lumi\u00e8re Lyon 2","ror":"https://ror.org/03rth4p18","country_code":"FR","type":"education","lineage":["https://openalex.org/I188626449","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Omar Boussaid","raw_affiliation_strings":["University of Lyon 2, Bron Cedex, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Lyon 2, Bron Cedex, France","institution_ids":["https://openalex.org/I188626449"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004311434","display_name":"Sabine Loudcher","orcid":"https://orcid.org/0000-0002-0494-0169","observed_orcids":["https://orcid.org/0000-0002-0494-0169"]},"institutions":[{"id":"https://openalex.org/I188626449","display_name":"Universit\u00e9 Lumi\u00e8re Lyon 2","ror":"https://ror.org/03rth4p18","country_code":"FR","type":"education","lineage":["https://openalex.org/I188626449","https://openalex.org/I203339264"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Sabine Loudcher Rabas\u00e9da","raw_affiliation_strings":["University of Lyon 2, Bron Cedex, France"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Lyon 2, Bron Cedex, France","institution_ids":["https://openalex.org/I188626449"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I188626449"],"apc_list":null,"apc_paid":null,"fwci":5.606,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.96077058,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"662","last_page":"667"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.22910000383853912,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11891","display_name":"Big Data and Business Intelligence","score":0.22910000383853912,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.17649999260902405,"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"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.14669999480247498,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/multiple-correspondence-analysis","display_name":"multiple correspondence analysis","score":0.9490000009536743},{"id":"https://openalex.org/keywords/olap","display_name":"OLAP","score":0.6970000267028809},{"id":"https://openalex.org/keywords/data-cubes","display_name":"data cubes","score":0.902999997138977},{"id":"https://openalex.org/keywords/high-dimensional-data-visualization","display_name":"high-dimensional data visualization","score":0.8109999895095825},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.902999997138977},{"id":"https://openalex.org/keywords/similarity-measures","display_name":"similarity measures","score":0.7630000114440918}],"concepts":[{"id":"https://openalex.org/C201932085","wikidata":"https://www.wikidata.org/wiki/Q642514","display_name":"Online analytical processing","level":3,"score":0.9079245328903198},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7390847206115723},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.6432920694351196},{"id":"https://openalex.org/C78168278","wikidata":"https://www.wikidata.org/wiki/Q5227269","display_name":"Data cube","level":2,"score":0.6409510374069214},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6248823404312134},{"id":"https://openalex.org/C3019022308","wikidata":"https://www.wikidata.org/wiki/Q1418353","display_name":"Multidimensional data","level":2,"score":0.5777566432952881},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5550386309623718},{"id":"https://openalex.org/C53051483","wikidata":"https://www.wikidata.org/wiki/Q861555","display_name":"Cube (algebra)","level":2,"score":0.5480515956878662},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5188121199607849},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5023984909057617},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4829072952270508},{"id":"https://openalex.org/C148298330","wikidata":"https://www.wikidata.org/wiki/Q1784754","display_name":"Correspondence analysis","level":2,"score":0.4757416248321533},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.4668847620487213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.45813727378845215},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3563576340675354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29928097128868103},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20149478316307068},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.18701842427253723},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12558168172836304},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09624987840652466},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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:10.1145/1150402.1150484","doi":"https://doi.org/10.1145/1150402.1150484","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1150402.1150484","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W152900593","https://openalex.org/W3150156619"],"related_works":["https://openalex.org/W7100490178","https://openalex.org/W2104704804","https://openalex.org/W1488506636","https://openalex.org/W7099891023","https://openalex.org/W1535176697","https://openalex.org/W1754682194","https://openalex.org/W2169468489","https://openalex.org/W2371689972","https://openalex.org/W2395481240","https://openalex.org/W7014160548"],"abstract_inverted_index":{"In":[0,27],"the":[1,34,63,93,96],"On":[2],"Line":[3],"Analytical":[4],"Processing":[5],"(OLAP)":[6],"context,":[7],"exploration":[8],"of":[9,45,65,83,98],"huge":[10],"and":[11,48,53,77,95],"sparse":[12],"data":[13,46,85,91],"cubes":[14,47],"is":[15,70],"a":[16,58,73,78,84],"tedious":[17],"task":[18],"which":[19],"does":[20],"not":[21],"always":[22],"lead":[23],"to":[24,41,61],"efficient":[25],"results.":[26],"this":[28],"paper,":[29],"we":[30],"couple":[31],"OLAP":[32],"with":[33],"Multiple":[35],"Correspondence":[36],"Analysis":[37],"(MCA)":[38],"in":[39],"order":[40],"enhance":[42],"visual":[43],"representations":[44],"thus,":[49],"facilitate":[50],"their":[51],"interpretations":[52],"analysis.":[54],"We":[55],"also":[56],"provide":[57],"quality":[59],"criterion":[60,69],"measure":[62],"relevance":[64],"obtained":[66],"representations.":[67],"The":[68],"based":[71],"on":[72,89],"geometric":[74],"neighborhood":[75],"concept":[76],"similarity":[79],"metric":[80],"between":[81],"cells":[82],"cube.":[86],"Experimental":[87],"results":[88],"real":[90],"proved":[92],"interest":[94],"efficiency":[97],"our":[99],"approach.":[100]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-10-26T11:51:55.731654","created_date":"2016-06-24T00:00:00"}