Simultaneous Clustering and Ranking from Pairwise Comparisons

When people make decisions with a number of ideas, designs, or other kinds of objects, one attempt is probably to organize them into several groups of objects and to prioritize them according to some preference. The grouping task is referred to as clustering and the prioritizing task is called as ranking. These tasks are often outsourced with the help of human judgments in the form of pairwise comparisons. Two objects are compared on whether they are similar in the clustering problem, while the object of higher priority is determined in the ranking problem. Our research question in this paper is whether the pairwise comparisons for clustering also help ranking (and vice versa). Instead of solving the two tasks separately, we propose a unified formulation to bridge the two types of pairwise comparisons. Our formulation simultaneously estimates the object embeddings and the preference criterion vector. The experiments using real datasets support our hypothesis; our approach can generate better neighbor and preference estimation results than the approaches that only focus on a single type of pairwise comparisons.

Application of Improved Intuitionistic Fuzzy for Sea-Battlefield Situation Assessment

Maritime Operations Command premise is a scientific and efficient assessment of the dynamic and varied sea-battlefield. According to research sea-battlefield situation assessment based on improved intuitionistic fuzzy algorithm based on projection. First is based on intuitionistic fuzzy to establish the sea-battlefield situation information matrix. Second is establishing the sea-battlefield assessment criterion vector. Finally, based on the theory of projection, it can compute the proximity of sea-battlefield situation information matrix and sea-battlefield assessment criterion vector, comprehensive and dynamic assess sea-battlefield in different time slices.

An Interactive Procedure to Solve Multi-Objective Decision-Making Problem: An Improvment to STEM Method

Decisions in the real-world contexts are often made in the presence of multiple, conflicting, and incommensurate criteria. Multiobjective programming methods such as multiple objective linear programming (MOLP) are techniques used to solve such multiple-criteria decision-making (MCDM) problems. One of the first interactive procedures to solve MOLP is STEM method. In this paper we try to improve STEM method in a way that we search a point in reduced feasible region whose criterion vector is closest to positive ideal criterion vector and furthest to negative ideal criterion vector. Therefore the presented method tries to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this paper.