Aggregating data is the main line of any discipline dealing with fusion of information from the knowledge-based systems to the decision-making. The purpose of aggregation methods is to convert a list of objects, all belonging to a given set, into a single representative object of the same set usually by an n-ary function, so-called aggregation operator. Since the useful aggregation functions for modeling real-life problems are limit, the basic problem is to construct a proper aggregation operator for each situation. During the last decades, a number of construction methods for aggregation functions have been developed to build new classes based on the well-known operators. This paper reviews some of these construction methods where they are based on transformation, composition and weighted rule.

In this study, we present the concept of interval-valued fuzzy soft point and then introduce the notions of neighborhood and quasi-neighbourhood of it in interval-valued fuzzy soft topological spaces. Separation axioms in interval-valued fuzzy soft topology, so-called $q$-$T_{i}$ for $ i=0,1,2,3,4 ,$ is introduced and some of its basic properties are also studied.