A Lattice-based Consensus Clustering Algorithm

P. 45–56.

Бочаров А. А., Gnatyshak D. V., Ignatov D. I., Mirkin B., Shestakoff A.

We propose a new algorithm for consensus clustering, FCA-Consensus, based on Formal Concept Analysis. As the input, the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after T runs from different initialisations. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal context objects×classes, where the classes are the set of all cluster labels from each initial k-means partition. We compare the results of the proposed algorithm in terms of ARI measure with the state-of-the-art algorithms on synthetic datasets. Under certain conditions, the best ARI values are demonstrated by FCA-Consensus. 

Keywords: анализ формальных понятий, решетки понятий, Formal Concept Analysis, concept lattices, consensus clustering, ensemble clustering, консенсусная кластеризация, partition lattices, решетки разбиений, lattice-based clustering, ансамблевая кластеризация, решеточная кластеризация

Publication based on the results of: Mining Data with Complex Structure and Semantic Technologies (2016)

In book

CLA 2016: Proceedings of the Thirteenth International Conference on Concept Lattices and Their Applications. CEUR Workshop Proceedings

Vol. 1624. , M.: Higher School of Economics, National Research University, 2016.