Trust relation, as defined in Social Network Analysis (SNA), is one of the recent notions considered decision making. This inspired our integration trust relation constructing a similarity–trust network. Similarity experts' preferences analyzed inclusively with by defining new combination function both attributes. The agglomerative hierarchical clustering approach applied to group experts into ...

The study of consensus in groups is fundamental to the understanding of group processes and the psychological experiences of individuals within groups. Measuring consensus in groups is tricky. This article reviews strengths and weaknesses of various methods for measuring the magnitude of consensus between persons on a single target belief. Considered are methods based on mean extremity, percent...

We consider grouping as a general characterization for problems such as clustering, community detection in networks, and multiple parametric model estimation. We are interested in merging solutions from different grouping algorithms, distilling all their good qualities into a consensus solution. In this paper, we propose a bi-clustering framework and perspective for reaching consensus in such g...

Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intuitiveness and robustness to noise corruptions. However, it aggressively tries...

A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensemble framework is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. To combine a set of partitions into one consensus partition, hierarchical clustering algorithms can be employed where first the E...

We consider ranking and clustering problems related to the aggregation of inconsistent information, in particular, rank aggregation, (weighted) feedback arc set in tournaments, consensus and correlation clustering, and hierarchical clustering. Ailon, Charikar, and Newman [4], Ailon and Charikar [3], and Ailon [2] proposed randomized constant factor approximation algorithms for these problems, w...

A resampling scheme for clustering with similarity to bootstrap aggregation (bagging) is presented. Bagging is used to improve the quality of pathbased clustering, a data clustering method that can extract elongated structures from data in a noise robust way. The results of an agglomerative optimization method are influenced by small fluctuations of the input data. To increase the reliability o...