{"id":"https://openalex.org/W2082144786","doi":"https://doi.org/10.1117/12.2040715","title":"Stars advantages vs parallel coordinates: shape perception as visualization reserve","display_name":"Stars advantages vs parallel coordinates: shape perception as visualization reserve","publication_year":2013,"publication_date":"2013-12-23","ids":{"openalex":"https://openalex.org/W2082144786","doi":"https://doi.org/10.1117/12.2040715","mag":"2082144786"},"language":"en","primary_location":{"id":"doi:10.1117/12.2040715","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2040715","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5049363818","display_name":"Vladimir Grishin","orcid":"https://orcid.org/0000-0002-9026-379X"},"institutions":[{"id":"https://openalex.org/I1325404993","display_name":"Forest Trends","ror":"https://ror.org/025ardq11","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1325404993"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Vladimir Grishin","raw_affiliation_strings":["View Trends International (United States)"],"affiliations":[{"raw_affiliation_string":"View Trends International (United States)","institution_ids":["https://openalex.org/I1325404993"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089775223","display_name":"Boris Kovalerchuk","orcid":"https://orcid.org/0000-0002-0995-9539"},"institutions":[{"id":"https://openalex.org/I29867402","display_name":"Central Washington University","ror":"https://ror.org/049c8eh51","country_code":"US","type":"education","lineage":["https://openalex.org/I29867402"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Boris Kovalerchuk","raw_affiliation_strings":["Central Washington Univ. (United States)","Central Washington University, United States"],"affiliations":[{"raw_affiliation_string":"Central Washington Univ. (United States)","institution_ids":["https://openalex.org/I29867402"]},{"raw_affiliation_string":"Central Washington University, United States","institution_ids":["https://openalex.org/I29867402"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5049363818"],"corresponding_institution_ids":["https://openalex.org/I1325404993"],"apc_list":null,"apc_paid":null,"fwci":0.8165,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.77229555,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"9017","issue":null,"first_page":"90170Q","last_page":"90170Q"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9975000023841858,"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"}},"topics":[{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.9975000023841858,"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"}},{"id":"https://openalex.org/T12417","display_name":"Morphological variations and asymmetry","score":0.9505000114440918,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9205999970436096,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/visualization","display_name":"Visualization","score":0.789762556552887},{"id":"https://openalex.org/keywords/gestalt-psychology","display_name":"Gestalt psychology","score":0.726087749004364},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6825392246246338},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5773101449012756},{"id":"https://openalex.org/keywords/data-visualization","display_name":"Data visualization","score":0.5601116418838501},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5245466828346252},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5093913674354553},{"id":"https://openalex.org/keywords/cartesian-coordinate-system","display_name":"Cartesian coordinate system","score":0.4995453357696533},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4685039818286896},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4176281690597534},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33608055114746094},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16675341129302979},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.11926078796386719}],"concepts":[{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.789762556552887},{"id":"https://openalex.org/C27362006","wikidata":"https://www.wikidata.org/wiki/Q272021","display_name":"Gestalt psychology","level":3,"score":0.726087749004364},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6825392246246338},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5773101449012756},{"id":"https://openalex.org/C172367668","wikidata":"https://www.wikidata.org/wiki/Q6504956","display_name":"Data visualization","level":3,"score":0.5601116418838501},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5245466828346252},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5093913674354553},{"id":"https://openalex.org/C16038011","wikidata":"https://www.wikidata.org/wiki/Q62912","display_name":"Cartesian coordinate system","level":2,"score":0.4995453357696533},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4685039818286896},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4176281690597534},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33608055114746094},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16675341129302979},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.11926078796386719},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2040715","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2040715","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W176891555","https://openalex.org/W655089096","https://openalex.org/W956365131","https://openalex.org/W1492240244","https://openalex.org/W1509461608","https://openalex.org/W1551726322","https://openalex.org/W1622945079","https://openalex.org/W2029176913","https://openalex.org/W2034694694","https://openalex.org/W2157379557","https://openalex.org/W2164973575","https://openalex.org/W6607237583","https://openalex.org/W6629331483","https://openalex.org/W6633136263","https://openalex.org/W6636781701","https://openalex.org/W6657887713","https://openalex.org/W6658922274"],"related_works":["https://openalex.org/W2013728941","https://openalex.org/W4225274103","https://openalex.org/W2579659702","https://openalex.org/W2154046714","https://openalex.org/W1965329638","https://openalex.org/W2189613078","https://openalex.org/W2923661510","https://openalex.org/W1574055964","https://openalex.org/W2542318691","https://openalex.org/W3160708108"],"abstract_inverted_index":{"Although":[0],"shape":[1,36,66],"perception":[2,37],"is":[3],"the":[4,71],"main":[5],"information":[6],"channel":[7],"for":[8,27,42,58,123],"brain,":[9],"it":[10],"has":[11],"been":[12,40],"poor":[13],"used":[14],"by":[15,63,70,102,120],"recent":[16,149],"visualization":[17,28,143],"techniques.":[18],"The":[19],"difficulties":[20],"of":[21,35,65,73,78,88,98,142],"its":[22,136],"modeling":[23],"are":[24,68,105,118],"key":[25],"obstacles":[26],"theory":[29],"and":[30,46,75,112,135],"application.":[31],"Known":[32],"experimental":[33,76],"estimates":[34],"capabilities":[38],"have":[39],"made":[41],"low":[43],"data":[44,53,60,89,128,155],"dimension,":[45],"they":[47],"were":[48],"usually":[49,151],"not":[50],"connected":[51],"with":[52,91,114,130,147],"structures.":[54],"More":[55],"applied":[56],"approach":[57],"certain":[59],"structures":[61,125],"detection":[62,126],"means":[64],"displays":[67,87,93],"considered":[69],"example":[72],"analytical":[74],"comparison":[77,146],"popular":[79],"now":[80],"Parallel":[81],"Coordinates":[82],"(PCs),":[83],"i.e.":[84],"2D":[85],"Cartesian":[86],"vectors,":[90],"polar":[92],"known":[94],"as":[95],"stars.":[96],"Advantages":[97],"stars":[99,115],"vs.":[100],"PCs":[101,117],"Gestalt":[103],"Laws":[104],"shown.":[106],"About":[107],"twice":[108],"faster":[109],"feature":[110],"selection":[111],"classification":[113],"than":[116],"showed":[119],"psychological":[121],"experiments":[122],"hyper-tubes":[124],"in":[127,145],"space":[129],"dimension":[131],"up":[132],"to":[133],"100-200":[134],"subspaces.":[137],"This":[138],"demonstrates":[139],"great":[140],"reserves":[141],"enhancement":[144],"many":[148],"techniques":[150],"focused":[152],"on":[153],"few":[154],"attributes":[156],"analysis.":[157]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}