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Synthesizing high-frequency rules from different data sources

机译：综合来自不同数据源的高频规则

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摘要

Many large organizations have multiple data sources, such as different branches of an interstate company. While putting all data together from different sources might amass a huge database for centralized processing, mining association rules at different data sources and forwarding the rules (rather than the original raw data) to the centralized company headquarter provides a feasible way to deal with multiple data source problems. In the meanwhile, the association rules at each data source may be required for that data source in the first instance, so association analysis at each data source is also important and useful. However, the forwarded rules from different data sources may be too many for the centralized company headquarter to use. This paper presents a weighting model for synthesizing high-frequency association rules from different data sources. There are two reasons to focus on high-frequency rules. First, a centralized company headquarter is interested in high-frequency rules because they are supported by most of its branches for corporate profitability. Second, high-frequency rules have larger chances to become valid rules in the union of all data sources. In order to extract high-frequency rules efficiently, a procedure of rule selection is also constructed to enhance the weighting model by coping with low-frequency rules. Experimental results show that our proposed weighting model is efficient and effective.
机译：许多大型组织都有多个数据源，例如一家州际公司的不同分支机构。虽然将来自不同来源的所有数据放在一起可能会聚集一个庞大的数据库以进行集中处理，但是在不同数据源处挖掘关联规则并将规则（而不是原始原始数据）转发到集中式公司总部可提供一种处理多个数据的可行方法源问题。同时，首先，该数据源可能需要每个数据源处的关联规则，因此，每个数据源处的关联分析也非常重要和有用。但是，来自不同数据源的转发规则对于集中式公司总部而言可能太多了。本文提出了一种加权模型，用于综合来自不同数据源的高频关联规则。关注高频规则有两个原因。首先，一个集中的公司总部对高频规则很感兴趣，因为高频规则受到其大多数分支机构的支持以提高公司的盈利能力。其次，高频规则在所有数据源的并集中更有可能成为有效规则。为了有效地提取高频规则，还构造了规则选择的过程以通过应对低频规则来增强加权模型。实验结果表明，我们提出的加权模型是有效的。

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