NEW FAST ALGORITHM FOR INCREMENTAL MINING OF ASSOCIATION RULES

Yasser El Sonbati, Rasha Kashef

2004

Abstract

Mining association rules is a well-studied problem, and several algorithms were presented for finding large itemsets. In this paper we present a new algorithm for incremental discovery of large itemsets in an increasing set of transactions. The proposed algorithm is based on partitioning the database and keeping a summary of local large itemsets for each partition based on the concept of negative border technique. A global summary for the whole database is also created to facilitate the fast updating of overall large itemsets. When adding a new set of transactions to the database, the algorithm uses these summaries instead of scanning the whole database, thus reducing the number of database scans. The results of applying the new algorithm showed that the new technique is quite efficient, and in many respects superior to other incremental algorithms like Fast Update Algorithm (FUP) and Update Large Itemsets (ULI).

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Paper Citation


in Harvard Style

El Sonbati Y. and Kashef R. (2004). NEW FAST ALGORITHM FOR INCREMENTAL MINING OF ASSOCIATION RULES . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 972-8865-00-7, pages 275-281. DOI: 10.5220/0002624102750281


in Bibtex Style

@conference{iceis04,
author={Yasser El Sonbati and Rasha Kashef},
title={NEW FAST ALGORITHM FOR INCREMENTAL MINING OF ASSOCIATION RULES},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2004},
pages={275-281},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002624102750281},
isbn={972-8865-00-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - NEW FAST ALGORITHM FOR INCREMENTAL MINING OF ASSOCIATION RULES
SN - 972-8865-00-7
AU - El Sonbati Y.
AU - Kashef R.
PY - 2004
SP - 275
EP - 281
DO - 10.5220/0002624102750281