Delivering Scalable Frequent Pattern Mining for Non-Expert Data Miners

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dc.contributor.authorHan, Zhao
dc.contributor.examiningcommitteeWang, Yang (Computer Science) Peng, Qingjin (Mechanical Engineering)en_US
dc.contributor.supervisorLeung, Carson K. (Computer Science)en_US
dc.date.accessioned2016-10-11T19:20:46Z
dc.date.available2016-10-11T19:20:46Z
dc.date.issued2016
dc.degree.disciplineComputer Scienceen_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.abstractAs a popular data mining task, frequent pattern mining has been proven to be help- ful for non-experts. For example, mining frequent purchased products helps store managers increase sales. As another example, finding popular courses assists uni- versity administrators arrange courses to avoid schedule conflicts. However, many data mining researchers have focused on improving algorithmic efficiency, but have put less focus on providing non-experts with a system designed specifically for these non-experts. In my M.Sc. thesis, I propose such a system, called PatternShow, which consists of (i) a user-friendly frontend web interface along with a visualization tool called BundleVis to show effectively frequent patterns for non-expert miners and (ii) a cloud-enabled backend that offers scalable frequent pattern mining. Results of my user study show the effectiveness of PatternShow in delivering scalable frequent pattern mining for non-expert data miners.en_US
dc.description.noteOctober 2016en_US
dc.identifier.urihttp://hdl.handle.net/1993/31885
dc.language.isoengen_US
dc.rightsopen accessen_US
dc.subjectData miningen_US
dc.subjectFrequent pattern miningen_US
dc.subjectFrequent pattern visualizationen_US
dc.titleDelivering Scalable Frequent Pattern Mining for Non-Expert Data Minersen_US
dc.typemaster thesisen_US
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