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A hierarchical approach to represent relational data applied to clustering tasks

Xavier, João C. and Canuto, Anne M.P. and Freitas, Alex A. and Gonçalves, Luis M.G. and Silla Jr, Carlos N. (2011) A hierarchical approach to represent relational data applied to clustering tasks. In: The 2011 International Joint Conference on Neural Networks. IEEE, pp. 182-196. ISBN 978-1-4244-9635-8. E-ISBN 978-1-4244-9637-2. (doi:10.1109/IJCNN.2011.6033624) (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)

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Official URL
http://dx.doi.org/10.1109/IJCNN.2011.6033624

Abstract

Nowadays, the representation of many real word problems needs to use some type of relational model. As a consequence, information used by a wide range of systems has been stored in multi relational tables. However, from a data mining point of view, it has been a problem, since most of the traditional data mining algorithms have not been originally proposed to handle this type of data without discarding relationship information. Aiming to ameliorate this problem, we propose a hierarchical approach for handling relational data. In this approach the relational data is converted into a hierarchical structure (the main table as the root and the relations as the nodes). This hierarchical way to represent relational data can be used either for classification or clustering purposes. In this paper, we will use it in clustering algorithms. In order to do so, we propose a hierarchical distance metric to compute the similarity between the tables. In the empirical analysis, we will apply the proposed approach in two well-known clustering algorithms (k-means and agglomerative hierarchical). Finally, this paper also compares the effectiveness of our approach with one existing relational approach.

Item Type: Book section
DOI/Identification number: 10.1109/IJCNN.2011.6033624
Uncontrolled keywords: clustering algorithms; indexes; sediments; measurement; relational databases; data mining
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: C.N. Silla-Junior
Date Deposited: 21 Sep 2012 09:49 UTC
Last Modified: 04 Feb 2020 04:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/30742 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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