Updating generalized association rules with evolving taxonomies |
| |
Authors: | Ming-Cheng Tseng Wen-Yang Lin Rong Jeng |
| |
Affiliation: | (1) Institute of Information Engineering, I-Shou University, Kaohsiung, 840, Taiwan;(2) Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan;(3) Department of Information Management, I-Shou University, Kaohsiung, 840, Taiwan |
| |
Abstract: | Mining generalized association rules among items in the presence of taxonomies has been recognized as an important model for
data mining. Earlier work on mining generalized association rules, however, required the taxonomies to be static, ignoring
the fact that the taxonomies of items cannot necessarily be kept unchanged. For instance, some items may be reclassified from
one hierarchy tree to another for more suitable classification, abandoned from the taxonomies if they will no longer be produced,
or added into the taxonomies as new items. Additionally, the analysts might have to dynamically adjust the taxonomies from
different viewpoints so as to discover more informative rules. Under these circumstances, effectively updating the discovered
generalized association rules is a crucial task. In this paper, we examine this problem and propose two novel algorithms,
called Diff_ET and Diff_ET2, to update the discovered frequent itemsets. Empirical evaluation shows that the proposed algorithms
are very effective and have good linear scale-up characteristics. |
| |
Keywords: | Data mining Generalized association rules Frequent itemsets Evolving taxonomies |
本文献已被 SpringerLink 等数据库收录! |
|