{"id":"https://openalex.org/W1964822354","doi":"https://doi.org/10.1145/1008694.1008701","title":"Discovering spatial patterns accurately with effective noise removal","display_name":"Discovering spatial patterns accurately with effective noise removal","publication_year":2004,"publication_date":"2004-06-13","ids":{"openalex":"https://openalex.org/W1964822354","doi":"https://doi.org/10.1145/1008694.1008701","mag":"1964822354"},"language":"en","primary_location":{"id":"doi:10.1145/1008694.1008701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1008694.1008701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery","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/A5055944639","display_name":"Qian Yu","orcid":"https://orcid.org/0000-0002-6224-5607"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yu Qian","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432244","display_name":"Kang Zhang","orcid":"https://orcid.org/0000-0003-3802-7535"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kang Zhang","raw_affiliation_strings":["The University of Texas at Dallas, Richardson, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Richardson, TX","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8602,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.72889865,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"43","last_page":"50"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983999729156494,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9983999729156494,"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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.9975000023841858,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.9919999837875366,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7948646545410156},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7508004903793335},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7459485530853271},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.663495659828186},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6546795964241028},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5646044611930847},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5462332963943481},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.532238245010376},{"id":"https://openalex.org/keywords/noise-measurement","display_name":"Noise measurement","score":0.5309811234474182},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.474764347076416},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.452555775642395},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.44428926706314087},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43429961800575256},{"id":"https://openalex.org/keywords/noisy-data","display_name":"Noisy data","score":0.41029679775238037},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4020032286643982},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.18265393376350403},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18192368745803833},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16924932599067688},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08420795202255249}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7948646545410156},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7508004903793335},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7459485530853271},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.663495659828186},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6546795964241028},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5646044611930847},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5462332963943481},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.532238245010376},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.5309811234474182},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.474764347076416},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.452555775642395},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.44428926706314087},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43429961800575256},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.41029679775238037},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4020032286643982},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.18265393376350403},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18192368745803833},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16924932599067688},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08420795202255249},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1145/1008694.1008701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1008694.1008701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.79.5156","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.79.5156","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.utdallas.edu/~kzhang/Publications/DMKD04.pdf","raw_type":"text"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-160023","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-160023","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W80917968","https://openalex.org/W194017790","https://openalex.org/W1494411790","https://openalex.org/W1673310716","https://openalex.org/W1987687547","https://openalex.org/W2065355661","https://openalex.org/W2127218421","https://openalex.org/W2141585940","https://openalex.org/W2148606255","https://openalex.org/W2150384816","https://openalex.org/W2160642098","https://openalex.org/W2319660501","https://openalex.org/W4247105055"],"related_works":["https://openalex.org/W2371336807","https://openalex.org/W4390662392","https://openalex.org/W71955863","https://openalex.org/W2359185137","https://openalex.org/W2610918223","https://openalex.org/W2085200861","https://openalex.org/W2789383625","https://openalex.org/W1575623062","https://openalex.org/W1522341235","https://openalex.org/W1927374526"],"abstract_inverted_index":{"Cluster":[0],"analysis":[1],"is":[2,20,84,118],"a":[3,56,81,97,115],"common":[4],"approach":[5],"to":[6,23,45,59,75,86,89,123],"pattern":[7],"discovery":[8],"in":[9],"spatial":[10],"databases.":[11],"While":[12],"many":[13],"clustering":[14,117],"techniques":[15],"have":[16],"been":[17],"developed,":[18],"it":[19],"still":[21],"challenging":[22],"discover":[24,124],"implicit":[25],"patterns":[26],"accurately":[27],"when":[28],"the":[29,61,87],"data":[30,47,67,94,110,122,133],"set":[31],"contains":[32],"two":[33],"kinds":[34],"of":[35,66],"noise":[36,43,91,107,113],"or":[37,51],"outliers:":[38],"1)":[39],"domain-specific":[40],"noise;":[41],"2)":[42],"similar":[44],"true":[46,93,109],"on":[48,120,130],"size,":[49],"shape,":[50],"density.":[52],"This":[53],"paper":[54],"presents":[55],"two-step":[57],"strategy":[58,98],"solve":[60],"problem":[62],"effectively:":[63],"firstly,":[64],"groups":[65],"points":[68],"are":[69],"separated":[70],"into":[71],"different":[72],"layers":[73],"according":[74],"their":[76],"sizes":[77],"and":[78,92,108,135],"densities;":[79],"then":[80],"layered":[82],"visualization":[83],"provided":[85],"user":[88],"separate":[90],"intuitively.":[95],"Such":[96],"not":[99],"only":[100],"produces":[101],"user-desired":[102],"results":[103],"but":[104],"also":[105],"separates":[106],"accurately.":[111],"After":[112],"removal,":[114],"hierarchical":[116],"performed":[119],"remaining":[121],"natural":[125],"clusters.":[126],"The":[127],"experimental":[128],"studies":[129],"both":[131],"benchmark":[132],"sets":[134],"real":[136],"images":[137],"show":[138],"very":[139],"encouraging":[140],"results.":[141]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}