Abstract In this paper, experimental data, given in the form of pairwise comparisons, such as distances or similarities, are considered. Clustering algorithms for processing data developed based on well-known k-means procedure. Relations to factor analysis shown. The problems improving clustering quality and finding proper number clusters case comparisons Illustrative examples provided.

We propose a new method for studying environments with unobserved individual heterogeneity. Based on model-implied pairwise inequalities, the classifies individuals in sample into groups defined by discrete heterogeneity unknown support. establish conditions under which are identified and consistently estimated through our method. show that performs well finite samples Monte Carlo simulation. t...

This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of n objects can be identified by standard sorting methods using n log2 n pairwise comparisons. We are interested in natural situations in which relationships among the objects may allow for ranking using far fewer pairwise comparisons. Specifically, w...

The ranking of n objects based on pairwise comparisons is a core machine learning problem, arising in recommender systems, ad placement, player ranking, biological applications and others. In many practical situations the true pairwise comparisons cannot be actively measured, but a subset of all n(n−1)/2 comparisons is passively and noisily observed. Optimization algorithms (e.g., the SVM) coul...