{"id":"https://openalex.org/W3017573653","doi":"https://doi.org/10.1109/civemsa45640.2019.9071597","title":"Attribute Selection Techniques to Clustering the Entrepreneurial Potential of Student based on Academic Behavior","display_name":"Attribute Selection Techniques to Clustering the Entrepreneurial Potential of Student based on Academic Behavior","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W3017573653","doi":"https://doi.org/10.1109/civemsa45640.2019.9071597","mag":"3017573653"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa45640.2019.9071597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5029973855","display_name":"Nova Rijati","orcid":"https://orcid.org/0000-0003-0870-4750"},"institutions":[{"id":"https://openalex.org/I4210127958","display_name":"Universitas Dian Nuswantoro","ror":"https://ror.org/02csxcg02","country_code":"ID","type":"education","lineage":["https://openalex.org/I4210127958"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Nova Rijati","raw_affiliation_strings":["Dept. of Informatics, Universitas Dian Nuswantoro, Semarang, Indonesia"],"affiliations":[{"raw_affiliation_string":"Dept. of Informatics, Universitas Dian Nuswantoro, Semarang, Indonesia","institution_ids":["https://openalex.org/I4210127958"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019052119","display_name":"Surya Sumpeno","orcid":"https://orcid.org/0000-0002-1744-1342"},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Sepuluh Nopember Institute of Technology","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Surya Sumpeno","raw_affiliation_strings":["Dept. of Electrical Engineering, Dept. of Computer Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Dept. of Computer Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012595591","display_name":"Mauridhi Hery Purnomo","orcid":"https://orcid.org/0000-0002-6221-7382"},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Sepuluh Nopember Institute of Technology","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Mauridhi Hery Purnomo","raw_affiliation_strings":["Dept. of Electrical Engineering, Dept. of Computer Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical Engineering, Dept. of Computer Engineering, Faculty of Electrical Technology, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5029973855"],"corresponding_institution_ids":["https://openalex.org/I4210127958"],"apc_list":null,"apc_paid":null,"fwci":1.0174,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.84620465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.8877000212669373,"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"}},"topics":[{"id":"https://openalex.org/T10538","display_name":"Data Mining Algorithms and Applications","score":0.8877000212669373,"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/T13373","display_name":"Data Mining and Machine Learning Applications","score":0.8820000290870667,"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/T12384","display_name":"Customer churn and segmentation","score":0.8521999716758728,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7771299481391907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7283759713172913},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5594964027404785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5305930376052856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49487727880477905},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4726090729236603},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4496777057647705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43776530027389526},{"id":"https://openalex.org/keywords/rough-set","display_name":"Rough set","score":0.4194675087928772},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"k-means clustering","score":0.41393354535102844},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.41007599234580994}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7771299481391907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7283759713172913},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5594964027404785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5305930376052856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49487727880477905},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4726090729236603},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4496777057647705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43776530027389526},{"id":"https://openalex.org/C111012933","wikidata":"https://www.wikidata.org/wiki/Q3137210","display_name":"Rough set","level":2,"score":0.4194675087928772},{"id":"https://openalex.org/C207968372","wikidata":"https://www.wikidata.org/wiki/Q310401","display_name":"k-means clustering","level":3,"score":0.41393354535102844},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.41007599234580994},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa45640.2019.9071597","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa45640.2019.9071597","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W221115402","https://openalex.org/W1499521858","https://openalex.org/W2099697766","https://openalex.org/W2109406886","https://openalex.org/W2564419218","https://openalex.org/W2587482202","https://openalex.org/W2608618990","https://openalex.org/W2785733888","https://openalex.org/W2793374418","https://openalex.org/W2886571391","https://openalex.org/W3012905637"],"related_works":["https://openalex.org/W2392963705","https://openalex.org/W4401883448","https://openalex.org/W2113424041","https://openalex.org/W2120258591","https://openalex.org/W2378227553","https://openalex.org/W1584028106","https://openalex.org/W2385548616","https://openalex.org/W4313193953","https://openalex.org/W2967215460","https://openalex.org/W1789480965"],"abstract_inverted_index":{"A":[0],"key":[1],"factor":[2],"in":[3,9,107,129],"the":[4,12,25,28,34,47,54,70,98,108,119,124,134],"process":[5],"of":[6,14,18,21,27,56,62,72,94,111,113],"knowledge":[7],"discovery":[8],"databases":[10],"is":[11,59,139],"quality":[13],"data":[15,41],"that":[16,23,103],"consists":[17],"a":[19],"set":[20],"attributes":[22,48,71,79],"explain":[24],"characteristics":[26],"data.":[29],"For":[30],"that,":[31],"we":[32],"need":[33],"right":[35],"attribute":[36,127,131],"selection":[37,132],"method":[38,105],"for":[39],"optimal":[40],"mining":[42],"performance.":[43],"In":[44],"this":[45],"case,":[46],"tested":[49],"with":[50,97],"machine":[51],"learning":[52],"are":[53],"result":[55],"mapping":[57],"factors":[58],"affecting":[60],"entrepreneurship":[61],"students":[63],"based":[64],"on":[65,69],"behavioral":[66],"science":[67],"theory":[68],"Indonesia":[73],"Higher":[74],"Education":[75],"Database.":[76],"Testing":[77],"dataset":[78],"using":[80],"four":[81],"different":[82],"methods,":[83],"namely":[84],"Correlation,":[85],"Information":[86],"Gain,":[87],"OneR,":[88],"and":[89],"Relief":[90],"F.":[91],"The":[92],"results":[93],"clustering":[95],"experiments":[96],"Simple":[99],"K-Means":[100],"algorithm":[101],"show":[102],"OneR":[104],"decrease":[106],"largest":[109],"drop":[110],"Sum":[112],"Squared":[114],"Errors":[115],"(17%)":[116],"compared":[117],"to":[118],"other":[120],"three":[121],"methods.":[122],"With":[123],"most":[125],"important":[126],"differences":[128],"each":[130],"method,":[133],"instances":[135],"cluster":[136],"profile":[137],"generated":[138],"also":[140],"different.":[141]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}