Relational Data Mining Through Extraction of Representative Exemplars

TL;DR

This paper introduces a method for extracting representative exemplars from relational datasets using a Borda aggregation-based measure, enabling network construction and applications like image summarization and co-author relation mining.

Contribution

It proposes a novel framework for identifying exemplars in relational data, defining a new measure of representativeness and demonstrating its application in network analysis.

Findings

Effective exemplar extraction from relational data

Successful application to image summarization

Mining co-authoring relationships in research teams

Abstract

With the growing interest on Network Analysis, Relational Data Mining is becoming an emphasized domain of Data Mining. This paper addresses the problem of extracting representative elements from a relational dataset. After defining the notion of degree of representativeness, computed using the Borda aggregation procedure, we present the extraction of exemplars which are the representative elements of the dataset. We use these concepts to build a network on the dataset. We expose the main properties of these notions and we propose two typical applications of our framework. The first application consists in resuming and structuring a set of binary images and the second in mining co-authoring relation in a research team.