preprints.org > computer science and mathematics > algebra and number theory > doi: 10.20944/preprints202011.0266.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 November 2020 / Approved: 9 November 2020 / Online: 9 November 2020 (08:48:40 CET)

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.

dyadic data; co-occurrence data; attributed dyadic data (ADD); mixture model; conditional mixture model (CMM); regression model

Computer Science and Mathematics, Algebra and Number Theory

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