Assessing Behaviour of Casino Patrons Using Clustering Methods
| dc.contributor.author | Morrissette, Samuel | |
| dc.contributor.examiningcommittee | Muthukumarana, Saman (Statistics) Fiege, Jason (Physics and Astronomy) Hu, Pingzhao (Biochemistry and Medical Genetics) Yang, Po (Statistics) | en_US |
| dc.contributor.examiningcommittee | Hu, Pingzhao (Biochemistry and Medical Genetics) Yang, Po (Statistics) | en_US |
| dc.contributor.supervisor | Muthukumarana, Saman (Statistics) Fiege, Jason (Physics and Astronomy) | en_US |
| dc.date.accessioned | 2021-09-07T20:52:17Z | |
| dc.date.available | 2021-09-07T20:52:17Z | |
| dc.date.copyright | 2021-08-23 | |
| dc.date.issued | 2021-08-23 | en_US |
| dc.date.submitted | 2021-08-23T15:40:25Z | en_US |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Research into statistical clustering techniques has grown tremendously in the past several years due to advances in both theory and computational capability. Despite this growth, there has been little documented research pertaining to clustering within the context of the casino industry - likely due to the proprietary nature of data. However, clustering results can help identify structure within a given dataset and provide valuable information to stakeholders. In particular, the segmentation of casino patrons may allow casino operators and researchers to develop insight into gambling behaviour and tendencies. Consequently, it is a worthwhile endeavour to explore the application of clustering methods to casino data. First, we discuss in detail several clustering algorithms along with a variety of metrics to assess clustering validity. Next, we apply these algorithms to casino data provided by a local industry partner and compare the resulting partitions. Furthermore, we examine strategies for interpreting these clustering results. Finally, we propose different candidate models which have satisfactory statistical performance and result in meaningful clusters from a pragmatic perspective. | en_US |
| dc.description.note | October 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35918 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Statistics, Clustering, Casinos | en_US |
| dc.title | Assessing Behaviour of Casino Patrons Using Clustering Methods | en_US |
| dc.type | master thesis | en_US |