{"id":{"repo_id":"purdue-thes","oai_identifier":"oai:docs.lib.purdue.edu:open_access_dissertations-2594"},"canonical_url":"https://search.dev.ndltd.org/etd/purdue-thes/oai:docs.lib.purdue.edu:open_access_dissertations-2594","repository":{"repo_id":"purdue-thes","name":"Purdue University","base_url":"https://docs.lib.purdue.edu/do/oai/"},"display":{"title":"GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY","abstract":"The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user's interest is an important task. Existing works on pattern","abstract_html":"The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user's interest is an important task. Existing works on pattern","abstract_has_math":false,"creators":["Bhuiyan, Md Mansurul Alam"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Mohammad A Hasan","Snehasis Mokhopadhyay","Chris Clifton","Elisa Bertino","Jean Honorio"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T03:54:38Z","subjects":["Interative Mining","MCMC Sampling","Personalized Patterns"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://docs.lib.purdue.edu/open_access_dissertations/1378","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mohammad A Hasan","Snehasis Mokhopadhyay","Chris Clifton","Elisa Bertino","Jean Honorio"]},{"key":"dc:creator","label":"Author","values":["Bhuiyan, Md Mansurul Alam"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Interative Mining","MCMC Sampling","Personalized Patterns"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://docs.lib.purdue.edu/open_access_dissertations/1378"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user's interest is an important task. Existing works on pattern"]},{"key":"dc:title","label":"Title","values":["GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY"]}]}],"canonical_facts":{"dc:contributor":["Mohammad A Hasan","Snehasis Mokhopadhyay","Chris Clifton","Elisa Bertino","Jean Honorio"],"dc:creator":["Bhuiyan, Md Mansurul Alam"],"dc:description.abstract":["The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user's interest is an important task. Existing works on pattern"],"dc:identifier":["https://docs.lib.purdue.edu/open_access_dissertations/1378"],"dc:subject":["Interative Mining","MCMC Sampling","Personalized Patterns"],"dc:title":["GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:54:38Z"}