Show simple item record

dc.contributor.authorSaha, Sajal
dc.date.accessioned2020-08-27T14:41:20Z
dc.date.available2020-08-27T14:41:20Z
dc.identifier.urihttp://hdl.handle.net/10464/14888
dc.description.abstractAssociation rules in data mining are implications between attributes of objects that hold in all instances of the given data. These rules are very useful to determine the properties of the data such as essential features of products that determine the purchase decisions of customers. Normally the data is given as binary (or crisp) tables relating objects with their attributes by yes-no entries. We propose a relational theory for generating attribute implications from many-valued contexts, i.e, where the relationship between objects and attributes is given by a range of degrees from no to yes. This degree is usually taken from a suitable lattice where the smallest element corresponds to the classical no and the greatest element corresponds to the classical yes. Previous related work handled many-valued contexts by transforming the context by scaling or by choosing a minimal degree of membership to a crisp (yes-no) context. Then the standard methods of formal concept analysis were applied to this crisp context. In our proposal, we will handle a many-valued context as is, i.e., without transforming it into a crisp one. The advantage of this approach is that we work with the original data without performing a transformation step which modifies the data in advance.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectdata miningen_US
dc.subjectformal concept analysisen_US
dc.subjectfuzzy formal concept analysisen_US
dc.subjectattribute implicationen_US
dc.subjectrelational algebraen_US
dc.titleData mining using L-fuzzy concept analysis.en_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-15T01:54:55Z


Files in this item

Thumbnail
Name:
Brock_Saha_Sajal_2020.pdf
Size:
568.6Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record