Novel methods of measuring the similarity and distance between complex fuzzy sets
This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, ma...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2016
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| Online Access: | https://eprints.nottingham.ac.uk/33401/ |
| _version_ | 1848794623770624000 |
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| author | McCulloch, Josie C. |
| author_facet | McCulloch, Josie C. |
| author_sort | McCulloch, Josie C. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare.
This thesis presents methods of comparing data that may be represented by such (complex) non-normal or non-convex fuzzy sets. The developed approaches for calculating relative comparisons also enable fusing methods of measuring similarity and distance between fuzzy sets. By using multiple methods, more meaningful comparisons of fuzzy sets are possible. Whereas if only a single type of measure is used, ambiguous results are more likely to occur.
This thesis provides a series of advances around the measuring of similarity and distance. Based on them, novel applications are possible, such as personalised and crowd-driven product recommendations. To demonstrate the value of the proposed methods, a recommendation system is developed that enables a person to describe their desired product in relation to one or more other known products. Relative comparisons are then used to find and recommend something that matches a person's subjective preferences. Demonstrations illustrate that the proposed method is useful for comparing complex, non-normal and non-convex fuzzy sets. In addition, the recommendation system is effective at using this approach to find products that match a given query. |
| first_indexed | 2025-11-14T19:19:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-33401 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T19:19:08Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-334012025-02-28T11:48:30Z https://eprints.nottingham.ac.uk/33401/ Novel methods of measuring the similarity and distance between complex fuzzy sets McCulloch, Josie C. This thesis develops measures that enable comparisons of subjective information that is represented through fuzzy sets. Many applications rely on information that is subjective and imprecise due to varying contexts and so fuzzy sets were developed as a method of modelling uncertain data. However, making relative comparisons between data-driven fuzzy sets can be challenging. For example, when data sets are ambiguous or contradictory, then the fuzzy set models often become non-normal or non-convex, making them difficult to compare. This thesis presents methods of comparing data that may be represented by such (complex) non-normal or non-convex fuzzy sets. The developed approaches for calculating relative comparisons also enable fusing methods of measuring similarity and distance between fuzzy sets. By using multiple methods, more meaningful comparisons of fuzzy sets are possible. Whereas if only a single type of measure is used, ambiguous results are more likely to occur. This thesis provides a series of advances around the measuring of similarity and distance. Based on them, novel applications are possible, such as personalised and crowd-driven product recommendations. To demonstrate the value of the proposed methods, a recommendation system is developed that enables a person to describe their desired product in relation to one or more other known products. Relative comparisons are then used to find and recommend something that matches a person's subjective preferences. Demonstrations illustrate that the proposed method is useful for comparing complex, non-normal and non-convex fuzzy sets. In addition, the recommendation system is effective at using this approach to find products that match a given query. 2016-07-15 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/33401/1/thesis.pdf McCulloch, Josie C. (2016) Novel methods of measuring the similarity and distance between complex fuzzy sets. PhD thesis, University of Nottingham. complex fuzzy sets |
| spellingShingle | complex fuzzy sets McCulloch, Josie C. Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title | Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title_full | Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title_fullStr | Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title_full_unstemmed | Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title_short | Novel methods of measuring the similarity and distance between complex fuzzy sets |
| title_sort | novel methods of measuring the similarity and distance between complex fuzzy sets |
| topic | complex fuzzy sets |
| url | https://eprints.nottingham.ac.uk/33401/ |