A Method for Selecting Suitable Records Based on Fuzzy Conformance and Aggregation Functions

Searching for suitable entities in a datasets is still a challenging task, because the entities’ attributes are often expressed by various data types including numerical, categorical, and fuzzy data. In addition, an attribute in a dataset may convey different data types for diverse records. In the query, the user may explain requirements by a different data type comparing with the one stored in a dataset, i.e. by linguistic term(s), whereas the respective attribute in a dataset is recorded as a real number and vice versa. Further, the user may provide complex preferences among atomic conditions. In this paper, we propose a robust framework capable to manage user requirements and match them with records in a dataset. The former is solved by the conformance measure, whereas for the latter different aggregation functions belonging to the conjunctive, averaging and hybrid classes have been suggested to cover particular aggregation needs like coalitions among atomic predicates and quantified conditions. The proposed method can be applied for selecting suitable records from any dataset containing numerical, categorical, fuzzy and binary data. The important characteristic of the presented method is the efficient applicability in the mentioned data collections, because conformance measure can manage different data types in the same manner. Finally, we discuss benefits, drawbacks and outline further activities.