Interesting Instance Discovery in Multi-Relational Data

Shou-de Lin

Thegeneral area of machine discovery focuses on methods to use computers to perform or assist discovery tasks. Herbert Simon described it as "gradual problemsolving processes of searching large problem spaces for incompletely defined goal objects." Today machine discovery research falls into two major categories, scientific discovery and knowledge discovery and data mining (KDD). In this paper we propose a new research direction that lies somewhere in-between these two trends: we call it interesting instance discovery (IID) which aims at discovering interesting instances in large, multi-relational datasets.


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