{"id":{"repo_id":"central-wash","oai_identifier":"oai:digitalcommons.cwu.edu:etd-1969"},"canonical_url":"https://search.dev.ndltd.org/etd/central-wash/oai:digitalcommons.cwu.edu:etd-1969","repository":{"repo_id":"central-wash","name":"Central Washington University","base_url":"https://digitalcommons.cwu.edu/do/oai/"},"display":{"title":"Decreasing Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery","abstract":"Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies pattern discovery in n-D data providing explainable rules in a visual form with significantly decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data.","abstract_html":"Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies pattern discovery in n-D data providing explainable rules in a visual form with significantly decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data.","abstract_has_math":false,"creators":["Gharawi, Abdulrahman Ahmed"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":null,"degree_discipline":"Computational Science","degree_department":null,"school":null,"contributors":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01T08:00:00Z","date_published":"2018-01-01T08:00:00Z","updated_at":"2026-07-24T01:37:23Z","subjects":["Visual Knowledge Discovery","Visual Analytics","Visual Data Mining","Occlusion","Interactive Visualization","Shifted Paired Coordinates","Artificial Intelligence and Robotics","Graphics and Human Computer Interfaces"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.cwu.edu/etd/941","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"]},{"key":"dc:creator","label":"Author","values":["Gharawi, Abdulrahman Ahmed"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Visual Knowledge Discovery","Visual Analytics","Visual Data Mining","Occlusion","Interactive Visualization","Shifted Paired Coordinates","Artificial Intelligence and Robotics","Graphics and Human Computer Interfaces"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.cwu.edu/etd/941"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies pattern discovery in n-D data providing explainable rules in a visual form with significantly decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data."]},{"key":"dc:title","label":"Title","values":["Decreasing Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery"]}]}],"canonical_facts":{"dc:contributor":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"],"dc:creator":["Gharawi, Abdulrahman Ahmed"],"dc:description.abstract":["Lack of explanation and occlusion are the major problems for interactive visual knowledge discovery, machine learning and data mining in multidimensional data. This thesis proposes a hybrid method that combines visual and analytical means to deal with these problems. This method, denoted as FSP, uses visualization of n-D data in 2-D in a set of Shifted Paired Coordinates (SPC). SPC for n-D data consists of n/2 pairs of Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point is represented as a directed graph in SPC. It is shown that the FSP method simplifies pattern discovery in n-D data providing explainable rules in a visual form with significantly decrease of the cognitive load for analysis of n-D data. The computational experiments on real data has shown its efficiency on both training and validation data."],"dc:identifier":["https://digitalcommons.cwu.edu/etd/941"],"dc:language":["English"],"dc:subject":["Visual Knowledge Discovery","Visual Analytics","Visual Data Mining","Occlusion","Interactive Visualization","Shifted Paired Coordinates","Artificial Intelligence and Robotics","Graphics and Human Computer Interfaces"],"dc:title":["Decreasing Occlusion and Increasing Explanation in Interactive Visual Knowledge Discovery"],"dc:type":["Text"],"thesis:degree_discipline":["Computational Science"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:37:23Z"}