{"id":{"repo_id":"unt","oai_identifier":"info:ark/67531/metadc4724"},"canonical_url":"https://search.dev.ndltd.org/etd/unt/info:ark/67531/metadc4724","repository":{"repo_id":"unt","name":"University of North Texas","base_url":"https://digital.library.unt.edu/oai/"},"display":{"title":"FP-tree Based Spatial Co-location Pattern Mining","abstract":"A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.","abstract_html":"A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.","abstract_has_math":false,"creators":["Yu, Ping"],"institution":"University of North Texas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Huang, Yan","Mikler, Armin R.","Brazile, Robert"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-05","date_published":"2005-05","updated_at":"2026-07-24T05:35:09Z","subjects":["Data mining.","Pattern perception.","FP-CM","frequent patterns","FP-tree","co-location pattern","spatial databases","spatial data mining"],"languages":["English"],"rights":["Public","Copyright","Yu, Ping","Copyright is held by the author, unless otherwise noted. All rights reserved."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 62161034","https://digital.library.unt.edu/ark:/67531/metadc4724/","ark: ark:/67531/metadc4724"],"render_values":[{"text":"oclc: 62161034","href":null,"code":true},{"text":"https://digital.library.unt.edu/ark:/67531/metadc4724/","href":"https://digital.library.unt.edu/ark:/67531/metadc4724/","code":true},{"text":"ark: ark:/67531/metadc4724","href":null,"code":true}]}]},"links":{"outbound_url":"https://doi.org/10.12794/metadc4724","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Yan","Mikler, Armin R.","Brazile, Robert"]},{"key":"dc:creator","label":"Author","values":["Yu, Ping"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2005-05"]},{"key":"dc:publisher","label":"Institution","values":["University of North Texas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Data mining.","Pattern perception.","FP-CM","frequent patterns","FP-tree","co-location pattern","spatial databases","spatial data mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["Public","Copyright","Yu, Ping","Copyright is held by the author, unless otherwise noted. 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This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:title","label":"Title","values":["FP-tree Based Spatial Co-location Pattern Mining"]}]}],"canonical_facts":{"dc:contributor":["Huang, Yan","Mikler, Armin R.","Brazile, Robert"],"dc:creator":["Yu, Ping"],"dc:date":["2005-05"],"dc:description":["A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude."],"dc:format":["Text"],"dc:identifier":["oclc: 62161034","doi: 10.12794/metadc4724","https://digital.library.unt.edu/ark:/67531/metadc4724/","ark: ark:/67531/metadc4724"],"dc:language":["English"],"dc:publisher":["University of North Texas"],"dc:rights":["Public","Copyright","Yu, Ping","Copyright is held by the author, unless otherwise noted. All rights reserved."],"dc:subject":["Data mining.","Pattern perception.","FP-CM","frequent patterns","FP-tree","co-location pattern","spatial databases","spatial data mining"],"dc:title":["FP-tree Based Spatial Co-location Pattern Mining"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:35:09Z"}