Relationship on the Relational Benefit Effect of Oil Products on Customer Satisfaction

In order to find out the relationship of relational benefit effect of oil products on customer satisfaction, the local fractional algorithm is proposed in this paper for data analysis. It is investigated the adjustment effect of alternative brand competitiveness and customer characteristics to this mechanism. The results show that the proposed algorithm can thus improve overall system performance substantially

Relationship on the Price Sensitivity and Actual Market Acceptance Degree of Metallic Materials

In order to find out the relationship between the price sensitivity and actual market acceptance degree of metallic materials, the database ensemble learning model is proposed in this paper. Due to the variety and class imbalance of customers, a database marketing model based on supervised clustering and ensemble learning is used for the model. The results show that the database ensemble learning model can thus improve the calculation accuracy and time-efficiency substantially

Exploring Unknown Universes in Probabilistic Relational Models

Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as "small universes are more likely". Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.Comment: Also accepted at the 9th StarAI Workshop at AAAI-2

Dimension Reduction via Colour Refinement

Colour refinement is a basic algorithmic routine for graph isomorphism testing, appearing as a subroutine in almost all practical isomorphism solvers. It partitions the vertices of a graph into "colour classes" in such a way that all vertices in the same colour class have the same number of neighbours in every colour class. Tinhofer (Disc. App. Math., 1991), Ramana, Scheinerman, and Ullman (Disc. Math., 1994) and Godsil (Lin. Alg. and its App., 1997) established a tight correspondence between colour refinement and fractional isomorphisms of graphs, which are solutions to the LP relaxation of a natural ILP formulation of graph isomorphism. We introduce a version of colour refinement for matrices and extend existing quasilinear algorithms for computing the colour classes. Then we generalise the correspondence between colour refinement and fractional automorphisms and develop a theory of fractional automorphisms and isomorphisms of matrices. We apply our results to reduce the dimensions of systems of linear equations and linear programs. Specifically, we show that any given LP L can efficiently be transformed into a (potentially) smaller LP L' whose number of variables and constraints is the number of colour classes of the colour refinement algorithm, applied to a matrix associated with the LP. The transformation is such that we can easily (by a linear mapping) map both feasible and optimal solutions back and forth between the two LPs. We demonstrate empirically that colour refinement can indeed greatly reduce the cost of solving linear programs