Abstract This paper presents several definitions of "optimal patterns" in triadic data and results of experimental comparison of five triclustering algorithms on real-world and synthetic datasets. The evaluation is carried over such criteria as resource efficiency, noise tolerance and quality scores involving cardinality, density, coverage, and diversity of the patterns. An ideal triadic pattern is a totally dense maximal cuboid (formal triconcept). Relaxations of this notion under consideration are: OAC-triclusters; triclusters optimal with respect to the leastsquare criterion; and graph partitions obtained by using spectral clustering. We show that searching for an optimal tricluster cover is an NP-complete problem, whereas determining the number of such covers is #P-complete. Our extensive computational experiments lead us to a clear strategy for choosing a solution at a given dataset guided by the principle of Pareto-optimality according to the proposed criteria.
In REF , the complexity of the problem of optimal triclustering cover with respect to several quality criteria is discussed; it is shown that the problem belongs to NP-complete complexity class whereas the problem of the number of such covers belongs to #P.
Triadic Formal Concept Analysis and triclustering: searching for optimal patterns
Machine Learning
Machine Learning
Mathematics Computer Science