Automatic construction of player categories using data clustering techniques
There is still much to be explored in the field of player modeling especially when it comes to classifying different players to their respective player categories. This becomes evident as most of the commercial games available today distinguish player types in a relatively shallow manner. These game...
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| Main Authors: | , , , |
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| Format: | text |
| Language: | English |
| Published: |
Animo Repository
2009
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/etd_bachelors/10625 |
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| Institution: | De La Salle University |
| Language: | English |
| Summary: | There is still much to be explored in the field of player modeling especially when it comes to classifying different players to their respective player categories. This becomes evident as most of the commercial games available today distinguish player types in a relatively shallow manner. These games usually offer users with only a choice of predetermined number of distinct difficulty levels (e.g. easy, medium, hard) which make game progression pre-set and linear. Moreover, most of these games' approaches for basic player categorization still lack an accurate basis. This research aims to provide an approach for automatically creating player categories that are constructed using data extracted from players. Three (3) different data clustering techniques (k-means, Agglomerative Clustering, Neural Networks) will be tested and analyzed to discover the advantages and disadvantages of each when used in creating the data-driven player categories. The test results will be summarized after resulting player categories from each clustering technique are compared and contrasted. |
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