A Novel Method for Evaluating Records from a Dataset using Interval Type-2 Fuzzy Sets

Miljan Vučetić, Aleksej Makarov


In this paper, we describe a method for evaluating suitable records from heterogeneous datasets based on interval type-2 fuzzy sets (IT2FSs). Retrieving records from a dataset including numerical, categorical, binary and fuzzy data in accordance with diverse user’s preferences is still a challenging task. The main challenge is how to deal with heterogeneity present when data in attribute values and user’s preferences are different by nature, e.g. when users explain their interests in linguistic term(s), whereas the attribute value is stored as a number and vice versa. Furthermore, a user may have different interests among desired preferences expressed with different data types. Using fuzzy theory can effectively help in handling heterogeneity in building robust query engines. This efficacy is mitigated when two or more values belong to an ordinary (type-1) fuzzy set with the same membership degree. We propose a solution based on IT2FSs, which are capable to better represent uncertainty in data and preferences. It efficiently improves the ranking of suitable records retrieved from datasets. The connection with aggregation of interval-valued data is also discussed.


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