Sequence data mining in cognitive science
| dc.contributor.advisor | Doebler, Philipp | |
| dc.contributor.author | Huang, He | |
| dc.contributor.referee | Pauly, Markus | |
| dc.date.accepted | 2024-09-30 | |
| dc.date.accessioned | 2024-10-16T07:14:46Z | |
| dc.date.available | 2024-10-16T07:14:46Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | This thesis summarizes my research work over a five-year period from February 2020 to August 2024, including all of the papers I published during that time. As it is a cumulative, this thesis provides a concise overview of the contributed articles, omitting exhaustive results and instead referring to the original publications for full details. The main text integrates these publications into a coherent narrative, starting with basic concepts and providing background on the respective research areas. For an in-depth discussion of specific research findings, readers are recommended to consult the relevant articles directly. This thesis covers the field of sequence data mining (SDM) in cognitive science. Cognitive science increasingly examines sequence data to understand cognitive tasks involving ordered steps or elements, such as language processing, decision-making, and memory formation. SDM techniques are used to uncover patterns and models within sequential data. However, modern data mining techniques like deep learning, which have been broadly applied in other domains, have not been fully integrated into traditional cognitive science tasks. Moreover, cognitive science deals with complex sequence data, such as scanpaths and trajectories, which pose challenges that traditional pattern discovery methods and modern techniques have not successfully overcome. This thesis aims to extend SDM methods in cognitive science by focusing on the application of advanced techniques and the creation of new methods specifically tailored for handling these complex, domain-specific sequences. For instance, a machine learning-based pipeline for automatic scoring in diversity thinking tasks is proposed in one of my published papers, utilizing algorithms such as Random Forest, XGBoost, and Support Vector Regression. Another two papers introduce novel approaches to analysis scanpaths and handwritten trajectories. Through experimental validation in each paper, the newly developed methods demonstrate superior performance compared to existing approaches. Overall, my research advances SDM by integrating modern data mining techniques to address the challenges posed by complex sequential data in cognitive science. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/42712 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-24547 | |
| dc.language.iso | en | de |
| dc.subject | Sequence data mining | en |
| dc.subject | Machine learning | en |
| dc.subject.ddc | 310 | |
| dc.subject.rswk | Kognitionswissenschaft | de |
| dc.subject.rswk | Data Mining | de |
| dc.subject.rswk | Maschinelles Lernen | de |
| dc.subject.rswk | Random Forest | de |
| dc.subject.rswk | Entscheidungsfindung | de |
| dc.title | Sequence data mining in cognitive science | en |
| dc.type | Text | de |
| dc.type.publicationtype | PhDThesis | de |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false | de |