一种面向布尔时间序列的关联规则挖掘算法

布尔时间序列中的关联规则挖掘较难处理,因为多数关联规则仅挖掘不同事务共同出现的规则,难以体现同一事件在不同时间内动态变化间的关联性.鉴于此,提出一种新的关联规则挖掘框架,利用常量化表示布尔数据的时间属性,结合聚类算法和关联分析,提高规则的支持度,从而解决布尔时间序列数据在关联规则挖掘中的时间值表示问题,并使用多种指标评价规则与传统算法比较.在真实的中风病预后好转数据预测中验证了所提出算法的有效性.

A Algorithm of Mining Association Rules for Binary Time Series

Association analysis for Binary time series is a difficult problem,because most of association rules lay emphasis on the relation among the items,but ignore the temporal correlation in the transaction database.Therefore,a new improved algorithm of mining association rules for Binary time series is presented to make use of both the relationship among the items and the temporality of association.By using the proposed algorithm,the Binary data is converted to common numerical value for representing the time-value implicitly,then clustering algorithm is combined with the association analysis,which improves the supports of most association rules.Several indicators are used to evaluate the results from the proposed algorithm.The experimental results on the prognosis dataset of stroke show the effectiveness of the proposed method.