Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern micro-dynamics networks. In this regard, using features obtained from network embedding predicting links has drawn widespread attention. Although methods based on edge or node similarity have been proposed to solve link problem, many technical challenges still exist due unique ...

The analysis of multiple DNA or protein sequences (I) Sequence similarity is an important issue in sequence alignments. In molecular biology, proteins and DNA can be similar with respect to their function, their structure, or their primary sequence of amino or nucleic acids. The general rule is that sequence determines shape, and shape determines function. So when we study sequence similarity, ...

An agent is asked to assess a real-valued variable Yp based on certain characteristics Xp = (X1 p , ...,X m p ), and on a database consisting (X1 i , ...,X m i , Yi) for i = 1, ..., n. A possible approach to combine past observations of X and Y with the current values of X to generate an assessment of Y is similarity-weighted averaging. It suggests that the predicted value of Y , Ȳ s p , be the...

There are two methods for GIS similarity measurement problem, one is cross-coefficient for GIS attribute similarity measurement, and the other is spatial autocorrelation that is based on spatial location. These methods can not calculate subzone similarity problem based on universal background. The rough measurement based on membership function solved this problem well. In this paper, we used ro...

A multidimensional theory of similarity in which the mental representations of stimulus objects are assumed to be drawn from multivariate normal distributions is described. A distance-based similarity function is defined and the expected value of similarity is derived. This theory is the basis for a possible explanation of paradoxical results with highly similar stimuli regarding the form of th...