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Authors: Alejandro García López 1 ; Rafael Berlanga 2 and Roxana Danger 2

Affiliations: 1 European Laboratory for Nuclear Research (CERN), Switzerland ; 2 Universitat Jaume I, Spain

ISBN: 978-972-8865-69-6

Keyword(s): Complex objects, association rules, clustering, data mining.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Acquisition ; Knowledge Engineering and Ontology Development ; Knowledge Representation ; Knowledge-Based Systems ; Symbolic Systems

Abstract: In this work we present a formal framework for mining complex objects, being those characterised by a set of heterogeneous attributes and their corresponding values. First we will do an introduction of the various Data Mining techniques available in the literature to extract association rules. We will as well show some of the drawbacks of these techniques and how our proposed solution is going to tackle them. Then we will show how applying a clustering algorithm as a pre-processing step on the data allow us to find groups of attributes and objects that will provide us with a richer starting point for the Data Mining process. Then we will define the formal framework, its decision functions and its interesting measurement rules, as well as a newly designed Data Mining algorithms specifically tuned for our objectives. We will also show the type of knowledge to be extracted in the form of a set of association rules. Finally we will state our conclusions and propose the future work.

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Paper citation in several formats:
García López A.; Berlanga R.; Danger R. and (2006). MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING.In Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT, ISBN 978-972-8865-69-6, pages 187-194. DOI: 10.5220/0001318401870194

@conference{icsoft06,
author={Alejandro {García López} and Rafael Berlanga and Roxana Danger},
title={MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING},
booktitle={Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,},
year={2006},
pages={187-194},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001318401870194},
isbn={978-972-8865-69-6},
}

TY - CONF

JO - Proceedings of the First International Conference on Software and Data Technologies - Volume 2: ICSOFT,
TI - MINING OF COMPLEX OBJECTS VIA DESCRIPTION CLUSTERING
SN - 978-972-8865-69-6
AU - García López, A.
AU - Berlanga, R.
AU - Danger, R.
PY - 2006
SP - 187
EP - 194
DO - 10.5220/0001318401870194

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