A New Measure for Analyzing and Fusing Sequences of Objects


This work is related to the combinatorial information analysis downside of seriation used for data visualization and exploratory analysis. Seriation re-sequences the info, so that more similar samples or objects seem nearer along, whereas dissimilar ones are more apart. Despite the large variety of current algorithms to understand such re-sequencing, there has not been a systematic means for analyzing the ensuing sequences, comparing them, or fusing them to obtain one unifying one. We tend to propose a brand new positional proximity measure that evaluates the similarity of two arbitrary sequences based on their agreement on pairwise positional information of the sequenced objects. Furthermore, we tend to present various statistical properties of this measure furthermore its normalized version modeled let's say of the generalized correlation coefficient. Primarily based on this measure, we outline a replacement procedure for consensus seriation that fuses multiple arbitrary sequences based on a quadratic assignment downside formulation and an economical manner of approximating its resolution. We tend to additionally derive theoretical links with alternative permutation distance functions and gift their associated combinatorial optimization forms for consensus tasks. The utility of the proposed contributions is demonstrated through the comparison and fusion of multiple seriation algorithms we have a tendency to have implemented, using many real-world datasets from totally different application domains.

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