{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/89051"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/89051","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Mining LEGO data sets to support LEGO design","abstract":"\"The Lego Group invented LEGO bricks. These bricks could be put up together to build creative LEGO sets of different themes, such as a Volkswagen T1 Camper Van model (in ``Sculpture\"\" theme) or The Simpsons House model (in ``Town\"\" theme). LEGO accompanies nearly everyone from youth to adulthood. The age groups of fans range from kids in pre-school to elder people who have grandchildren. With such a strong and huge fan base, there appear a lot of websites that provide LEGO data of sets, parts, minifigures as well as online communities for fans to share their experience about LEGO sets. However, there barely exists any research in this rich data source to discover knowledge and insights about how each LEGO part plays a role in a LEGO set, in its own part category and in a LEGO theme; how each LEGO set is different from the other one in the aspects of theme and part components. There are a lot of interesting questions we can address from the datasets that will not only help better LEGO designs, but will also help LEGO fans or potential customers make efficient purchasing decisions when they get more familiar with LEGO sets and parts. To address these needs, in this thesis, we propose a systematic method of mining LEGO datasets of sets and parts to support LEGO design. Treating each LEGO set as a document and each part in it as a word, we are able to apply data mining techniques, such as Topic Model and K-Means Clustering to find statistics of sets and parts. The preliminary experiment results show that the proposed methods can automatically construct a LEGO Brick Lexicon that shows a part's relationship with other parts, sets and themes, discover knowledge about typical LEGO construction patterns and create hybrid theme recommendations. We believe this is a step forward to helping LEGO designers create more attractive sets with pragmatic parts as well as improving the building experience of LEGO fans/builders.\"","abstract_html":""The Lego Group invented LEGO bricks. These bricks could be put up together to build creative LEGO sets of different themes, such as a Volkswagen T1 Camper Van model (in ``Sculpture"" theme) or The Simpsons House model (in ``Town"" theme). LEGO accompanies nearly everyone from youth to adulthood. The age groups of fans range from kids in pre-school to elder people who have grandchildren. With such a strong and huge fan base, there appear a lot of websites that provide LEGO data of sets, parts, minifigures as well as online communities for fans to share their experience about LEGO sets. However, there barely exists any research in this rich data source to discover knowledge and insights about how each LEGO part plays a role in a LEGO set, in its own part category and in a LEGO theme; how each LEGO set is different from the other one in the aspects of theme and part components. There are a lot of interesting questions we can address from the datasets that will not only help better LEGO designs, but will also help LEGO fans or potential customers make efficient purchasing decisions when they get more familiar with LEGO sets and parts. To address these needs, in this thesis, we propose a systematic method of mining LEGO datasets of sets and parts to support LEGO design. Treating each LEGO set as a document and each part in it as a word, we are able to apply data mining techniques, such as Topic Model and K-Means Clustering to find statistics of sets and parts. The preliminary experiment results show that the proposed methods can automatically construct a LEGO Brick Lexicon that shows a part's relationship with other parts, sets and themes, discover knowledge about typical LEGO construction patterns and create hybrid theme recommendations. We believe this is a step forward to helping LEGO designers create more attractive sets with pragmatic parts as well as improving the building experience of LEGO fans/builders."","abstract_has_math":false,"creators":["Zhang, Xiaodan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Zhai, ChengXiang"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-03-02T19:34:21Z","date_published":"2016-03-02T19:34:21Z","updated_at":"2026-07-22T22:26:32Z","subjects":["Data Mining","Information Retrieval","Knowledge Discovery","LEGO"],"languages":["en"],"rights":["Copyright 2015 Xiaodan Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/89051","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Zhai, ChengXiang"]},{"key":"dc:creator","label":"Author","values":["Zhang, Xiaodan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-03-02T19:34:21Z","2015-12-07","2015-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data Mining","Information Retrieval","Knowledge Discovery","LEGO"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Xiaodan Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/89051"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["\"The Lego Group invented LEGO bricks. These bricks could be put up together to build creative LEGO sets of different themes, such as a Volkswagen T1 Camper Van model (in ``Sculpture\"\" theme) or The Simpsons House model (in ``Town\"\" theme). LEGO accompanies nearly everyone from youth to adulthood. The age groups of fans range from kids in pre-school to elder people who have grandchildren. With such a strong and huge fan base, there appear a lot of websites that provide LEGO data of sets, parts, minifigures as well as online communities for fans to share their experience about LEGO sets. However, there barely exists any research in this rich data source to discover knowledge and insights about how each LEGO part plays a role in a LEGO set, in its own part category and in a LEGO theme; how each LEGO set is different from the other one in the aspects of theme and part components. There are a lot of interesting questions we can address from the datasets that will not only help better LEGO designs, but will also help LEGO fans or potential customers make efficient purchasing decisions when they get more familiar with LEGO sets and parts. To address these needs, in this thesis, we propose a systematic method of mining LEGO datasets of sets and parts to support LEGO design. Treating each LEGO set as a document and each part in it as a word, we are able to apply data mining techniques, such as Topic Model and K-Means Clustering to find statistics of sets and parts. The preliminary experiment results show that the proposed methods can automatically construct a LEGO Brick Lexicon that shows a part's relationship with other parts, sets and themes, discover knowledge about typical LEGO construction patterns and create hybrid theme recommendations. We believe this is a step forward to helping LEGO designers create more attractive sets with pragmatic parts as well as improving the building experience of LEGO fans/builders.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Xiaodan Zhang, accepted the attached license on 2015-12-04 at 14:05.","The student, Xiaodan Zhang, submitted this Thesis for approval on 2015-12-04 at 14:20.","This Thesis was approved for publication on 2015-12-07 at 13:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8937 on 2016-03-02 at 12:51:29","Made available in DSpace on 2016-03-02T19:34:21Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2015.pdf: 1203528 bytes, checksum: 2ba09a84a6b8855ca41ab8d818c6144b (MD5) LICENSE.txt: 4210 bytes, checksum: b6bc5ddaf096c70b4382f8e29d0b59b1 (MD5) Previous issue date: 2015-12-07"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Mining LEGO data sets to support LEGO design"]}]}],"canonical_facts":{"dc:contributor":["Zhai, ChengXiang"],"dc:creator":["Zhang, Xiaodan"],"dc:date":["2016-03-02T19:34:21Z","2015-12-07","2015-12"],"dc:description":["\"The Lego Group invented LEGO bricks. These bricks could be put up together to build creative LEGO sets of different themes, such as a Volkswagen T1 Camper Van model (in ``Sculpture\"\" theme) or The Simpsons House model (in ``Town\"\" theme). LEGO accompanies nearly everyone from youth to adulthood. The age groups of fans range from kids in pre-school to elder people who have grandchildren. With such a strong and huge fan base, there appear a lot of websites that provide LEGO data of sets, parts, minifigures as well as online communities for fans to share their experience about LEGO sets. However, there barely exists any research in this rich data source to discover knowledge and insights about how each LEGO part plays a role in a LEGO set, in its own part category and in a LEGO theme; how each LEGO set is different from the other one in the aspects of theme and part components. There are a lot of interesting questions we can address from the datasets that will not only help better LEGO designs, but will also help LEGO fans or potential customers make efficient purchasing decisions when they get more familiar with LEGO sets and parts. To address these needs, in this thesis, we propose a systematic method of mining LEGO datasets of sets and parts to support LEGO design. Treating each LEGO set as a document and each part in it as a word, we are able to apply data mining techniques, such as Topic Model and K-Means Clustering to find statistics of sets and parts. The preliminary experiment results show that the proposed methods can automatically construct a LEGO Brick Lexicon that shows a part's relationship with other parts, sets and themes, discover knowledge about typical LEGO construction patterns and create hybrid theme recommendations. We believe this is a step forward to helping LEGO designers create more attractive sets with pragmatic parts as well as improving the building experience of LEGO fans/builders.\"","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2016-03-02 without embargo terms","The student, Xiaodan Zhang, accepted the attached license on 2015-12-04 at 14:05.","The student, Xiaodan Zhang, submitted this Thesis for approval on 2015-12-04 at 14:20.","This Thesis was approved for publication on 2015-12-07 at 13:58.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8937 on 2016-03-02 at 12:51:29","Made available in DSpace on 2016-03-02T19:34:21Z (GMT). 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