Visualization for frequent pattern mining

dc.contributor.authorHoi, Calvin Soen Him
dc.contributor.examiningcommitteeWang, Yang (Computer Science)en_US
dc.contributor.examiningcommitteeWang, Liqun (Statistics)en_US
dc.contributor.supervisorLeung, Carson K. (Computer Science)en_US
dc.date.accessioned2020-09-09T17:25:29Z
dc.date.available2020-09-09T17:25:29Z
dc.date.copyright2020-08-26
dc.date.issued2020-08-26en_US
dc.date.submitted2020-08-26T22:09:38Zen_US
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractIn this fast information-technological world, data grow bigger and bigger. Big data refer to the huge volume, high velocity, wide variety and different veracity of the data flow. One of the big challenges is to aggregate and visualize the data mining results to the end user. To achieve a certain level of data validity for better data aggregation in estimation and prediction, the synergy of data visualization models and data mining strategies are necessary. In this MSc thesis, the focus is on frequent pattern visualization. This aims to visualize textual frequent patterns, which result in graphical representation with three important information: frequency distribution, cardinality distribution and superset-subset relationship. The designed and implemented visualization system for frequent pattern mining reveals interesting information and useful knowledge mined from the transactional databases for various applications and services.en_US
dc.description.noteOctober 2020en_US
dc.identifier.urihttp://hdl.handle.net/1993/35033
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectData miningen_US
dc.subjectData visualizationen_US
dc.subjectVisual analyticsen_US
dc.subjectData scienceen_US
dc.subjectFrequent patternsen_US
dc.subjectAssociation rulesen_US
dc.subjectCOVID-19en_US
dc.titleVisualization for frequent pattern miningen_US
dc.typemaster thesisen_US
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