首页> 外文会议>ESF Exploratory Workshop on Pattern Detection and Discovery, Sep 16-19, 2002, London, UK >Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining
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Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining

机译:基于约束的发现和归纳查询:在关联规则挖掘中的应用

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Recently inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. Querying these databases needs for primitives to: (1) select, manipulate and query data, (2) select, manipulate and query "interesting" patterns (i.e., those patterns that satisfy certain constraints), and (3) cross over patterns and data (e.g., selecting the data in which some patterns hold). Designing such query languages is a long-term goal and only preliminary approaches have been studied, mainly for the association rule mining task. Starting from a discussion on the MINE RULE operator, we identify several open issues for the design of inductive databases dedicated to these descriptive rules. These issues concern not only the offered primitives but also the availability of efficient evaluation schemes. We emphasize the need for primitives that work on more or less condensed representations for the frequent itemsets, e.g., the (frequent) δ-free and closed itemsets. It is useful not only for optimizing single association rule mining queries but also for sophisticated post-processing and interactive rule mining.
机译:最近提出了归纳数据库(IDB),以解决从大型数据库中发现知识的问题。查询这些数据库需要的原语包括:(1)选择,操纵和查询数据,(2)选择,操纵和查询“有趣的”模式(即,满足某些约束的那些模式),以及(3)跨越模式和数据(例如,选择其中包含某些模式的数据)。设计这样的查询语言是一个长期目标,仅研究了初步的方法,主要用于关联规则挖掘任务。从有关MINE RULE运算符的讨论开始,我们为归纳这些描述性规则的归纳数据库的设计确定了几个未解决的问题。这些问题不仅涉及所提供的原语,而且还涉及有效评估方案的可用性。我们强调需要用于或多或少精简表示频繁项目集(例如(频繁)无δ和封闭项目集)的基元的需求。它不仅对优化单个关联规则挖掘查询有用,而且对复杂的后处理和交互式规则挖掘也很有用。

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