Different Types of Search Algorithms for Rough Sets
Based on the available information in many cases it can happen that two objects cannot be distinguished. If a set of data is given and in this set
two objects have the same attribute values, then these two objects are called indiscernible. This indiscernibility has an effect on the membership relation,
because in some cases it makes our judgment uncertain about a given object. The uncertainty appears because if something about an object is needed to be
stated, then all the objects that are indiscernible from the given object must be taken into consideration. The indiscernibility relation is an equivalence
relation which represents background knowledge embedded in an information system. In a Pawlakian system this relation is used in set approximation.
Correlation clustering is a clustering technique which generates a partition. In the authors’ previous research the possible usage of the correlation clustering
in rough set theory was investigated. In this paper the authors show how different types of search algorithms affect the set approximation.
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