MR-Radix: a multi-relational data mining algorithm
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
14/10/2013
14/10/2013
2012
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Resumo |
Abstract Background Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine - an traditional algorithm - and its corresponding multi-relational proposed, MR-Radix. Results This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multi-relational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases. This project was financed by CAPES. We thank David R. M. Mercer for English language review and translation. |
Identificador |
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES, HEIDELBERG, v.2, MAR 07, 2012 2192-1962 |
Idioma(s) |
eng |
Relação |
HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES |
Direitos |
openAccess Valêncio et al; licensee Springer. - This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Palavras-Chave | #MR-RADIX #MULTI-RELATIONAL DATA MINING #ASSOCIATION RULES #MINING FREQUENT ITEMSETS #RELATIONAL DATABASES |
Tipo |
article original article publishedVersion |