The specific golf swing patterns using K-means clustering with the two-sided confidence interval

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dc.contributor.authorTangwongcharoen, Wisan
dc.contributor.authorTitiroongruang, Wisut
dc.date.accessioned2026-08-06T10:17:49Z
dc.date.available2026-08-06T10:17:49Z
dc.date.issued2017-11-03
dc.description.abstractGolf is a popular sport for exercise or socializing. It affects an increasing number of patients. Because of these reasons the researchers decided to focus on this problem. We presented the analysis golf swing using K-Means Clustering with Two-Sided Confidence Intervals and the Closest Pair of Points Problem. The raw data were clustered by K-Means Clustering. The boundaries of subgroups processed by K-Means Clustering were calculated to represent the data of the normal and abnormal golfers, which we use as the diagnose patterns. The compared data is the comparison of the diagnose patterns of both normal and abnormal patterns. From the experimental results, the percentage of similar pattern is shown. Therefore, this algorithm can help the doctor to predict the injuries trend of golf players.
dc.identifier.citationEcti Con 2017 2017 14th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology, 698-701, 2017
dc.identifier.doi10.1109/ECTICon.2017.8096334
dc.identifier.other2-s2.0-85039909720
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7967
dc.sourceEcti Con 2017 2017 14th International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology
dc.subjectClosest Pair of Points Problem
dc.subjectK-Means Clustering
dc.subjectSpecific Golf Swing Patterns
dc.subjectTwo-Sided Confidence Interval
dc.titleThe specific golf swing patterns using K-means clustering with the two-sided confidence interval
dc.typeConference Paper

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