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Are we really discovering ''interesting'' knowledge from data?

Freitas, Alex A. (2006) Are we really discovering ''interesting'' knowledge from data? Expert Update (the BCS-SGAI Magazine), 9 (1). pp. 41-47. ISSN 1465-4091. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14404)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

Abstract

This paper is a critical review of the literature on discovering comprehensible, interesting knowledge (or patterns) from data. The motivation for this review is that the majority of the literature focuses only on the problem of maximizing the accuracy of the discovered patterns, ignoring other important pattern-quality criteria that are user-oriented, such as comprehensibility and interestingness. The word “interesting” has been used with several different meanings in the data mining literature. In this paper interesting essentially means novel or surprising. Although comprehensibility and interestingness are considerably harder to measure in a formal way than accuracy, they seem very relevant criteria to be considered if we are serious about discovering knowledge that is not only accurate, but also useful for human decision making. The paper discusses both data-driven methods (based mainly on statistical properties of the patterns) and user-driven methods (which take into account the user’s background knowledge or believes) for discovering interesting knowledge. Data-driven methods are discussed in more detail because they are more common in the literature and are more controversial. The paper also suggests future research directions in the discovery of interesting knowledge.

Item Type: Article
Uncontrolled keywords: data mining, knowledge discovery
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14404 (The current URI for this page, for reference purposes)

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