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    • 4. 发明授权
    • Method and system for mining weighted association rule
    • 挖掘加权关联规则的方法和系统
    • US06415287B1
    • 2002-07-02
    • US09487797
    • 2000-01-20
    • Wei WangJiong YangPhilip Shi-Lung Yu
    • Wei WangJiong YangPhilip Shi-Lung Yu
    • G06F1730
    • G06N5/025G06F2216/03Y10S707/99931Y10S707/99933Y10S707/99936Y10S707/99937Y10S707/99942Y10S707/99943
    • The traditional association rule problem is extended by allowing a weight to be associate with each item in a transaction to reflect interest/intensity of each item within the transaction. The weighted association rules from a set of tuple lists are discovered, where each tuple consists of an item and an associated weight and each tuple list consists multiple tuples. The weighted association rules (WARs) are generated where some subset of items forms the consequent part of the rule (i.e., right hand side of the rule) and some other (non-overlapped) subset of items from the antecedent part of the rule (i.e., left hand side of the rule). The range of the weight value of each item is specified in the said rule such that the number of tuples satisfying both the left hand side and right hand side of the rules exceeds a pre-specified support level (referred to as the support requirement) and the fraction of tuples satisfying the left hand side also satisfying the right hand side exceeds a pre-specified confidence level (referred to as the confidence requirement).
    • 通过允许将权重与交易中的每个项目相关联来反映交易中每个项目的兴趣/强度来扩展传统的关联规则问题。 发现来自一组元组列表的加权关联规则,其中每个元组由一个项目和相关联的权重组成,并且每个元组列表都包含多个元组。 产生加权关联规则(WAR),其中某些项目子集形成规则的后续部分(即规则的右侧)以及来自规则的先前部分的某些其他(非重叠的)子集( 即规则的左手边)。 在所述规则中规定了每个项目的权重值的范围,使得满足规则的左手侧和右手侧的元组的数量超过预定的支持水平(称为支持要求),并且 满足左手侧的满足右手侧的元组的分数超过预定的置信水平(称为置信度要求)。
    • 9. 发明授权
    • System and method for meta-pattern discovery
    • 元模式发现的系统和方法
    • US06785663B2
    • 2004-08-31
    • US09752620
    • 2000-12-28
    • Wei WangJiong YangPhilip Shi-Lung Yu
    • Wei WangJiong YangPhilip Shi-Lung Yu
    • G06N502
    • G06K9/00523
    • Periodic patterns in time series data can be hierarchical in nature, where a higher level pattern may comprise repetitions of lower level patterns. In the presence of noises, these repetitions of lower level patterns may not be perfect. A novel model, namely a meta-pattern, is provided in accordance with the present invention to capture these higher level patterns. The meta-pattern can not only provide a more compact representation of patterns but also capture the regularities of pattern evolutions, which may not be expressed by previous models due to the presence of noise. A method is provided to mine meta-patterns in an iterative manner by discovering meta-patterns and their supporting subsequences in the form of lists of segments of contiguous repetitions of a meta-pattern. The number of pattern repetitions in each said segment is at least a predefined threshold min_rep and the distance between any two adjacent segments is at most a predefined threshold max_dis.
    • 时间序列数据中的周期性模式本质上可以是分级的,其中较高级别的模式可以包括较低级别模式的重复。 在有噪音的情况下,这些重复的较低级别的模式可能不完美。 根据本发明提供了一种新颖的模型,即元模式,以捕获这些更高级别的模式。 元模式不仅可以提供更紧凑的图案表示,而且可以捕获模式演化的规律性,由于存在噪声,可能不会由以前的模型表示。 提供了一种通过以元模式的连续重复的分段的列表的形式发现元模式及其支持子序列以迭代方式来挖掘元模式的方法。 每个所述段中的模式重复次数至少为预定义的阈值min_rep,并且任何两个相邻段之间的距离至多为预定阈值max_dis。