New Trends in Knowledge Driven Data Mining

Cláudia Antunes, Andreia Silva

Abstract

Existing mining algorithms, from classification to pattern mining, reached considerable levels of efficiency, and their extension to deal with more demanding data, such as data streams and big data, show their incontestable quality and adequacy to the problem. Despite their efficiency, their effectiveness on identifying useful information is somehow impaired, not allowing for making use of existing domain knowledge to focus the discovery. The use of this knowledge can bring significant benefits to data mining applications, by resulting in simpler and more interesting and usable models. However, most of existing approaches are concerned with being able to mine specific domains, and therefore are not easily reusable, instead of building general algorithms that are able to incorporate domain knowledge, independently of the domain. In our opinion, this requires a drift in the focus of the research in data mining, and we argue this change should be from domain-driven to knowledge-driven data mining, aiming for a stronger emphasis on the exploration of existing domain knowledge for guiding existing algorithms.

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Paper Citation


in Harvard Style

Antunes C. and Silva A. (2014). New Trends in Knowledge Driven Data Mining . In Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-027-7, pages 346-351. DOI: 10.5220/0004974003460351


in Bibtex Style

@conference{iceis14,
author={Cláudia Antunes and Andreia Silva},
title={New Trends in Knowledge Driven Data Mining},
booktitle={Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2014},
pages={346-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004974003460351},
isbn={978-989-758-027-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - New Trends in Knowledge Driven Data Mining
SN - 978-989-758-027-7
AU - Antunes C.
AU - Silva A.
PY - 2014
SP - 346
EP - 351
DO - 10.5220/0004974003460351