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首页> 外文期刊>The international arab journal of information technology >Rule Schema Multi-Level for Local Patterns Analysis: Application in Production Field
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Rule Schema Multi-Level for Local Patterns Analysis: Application in Production Field

机译：局部模式分析的规则架构多级：在生产领域中的应用

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Recently, Multi-Database Mining (MDBM) for association rules has been recognized as an important and timely research area in the Knowledge Discovery Database (KDD) community. It consists of mining different databases in order to obtain frequent patterns which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [8,9,13,14,15,16] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results, ie., the results that would be obtained if all the databases are put together and mining has been done. When the pattern is present in a site but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore this process can lose some interesting patterns which can help the decision maker to make the right decisions. To adress this problem, we propose to integrate the users knowledge in the local and global mining process. For that we describe the users beliefs and expectation by the rule schemas multi-level and integrate them in both the local association rules mining and in the synthesizing process. In this situation we get true global patterns of select items as there is no need to estimate them. Furthermore, a novel Condensed Patterns Tree (CP-TREE)structure is defined in order to store the candidates patterns for all organization levels which can improve the time processing and reduce the space requirement. In addition CP-TREE structure facilitate the exploration and the projection of the candidates patterns in differents levels. finally We conduct some experimentations in real world databases which are the production field and demonstrate the effectivlness of the CP-TREE structure on time processing and space requirement.
机译：最近，用于关联规则的多数据库挖掘（MDBM）在知识发现数据库（KDD）社区中被认为是重要且及时的研究领域。它包括挖掘不同的数据库以获得频繁的模式，这些模式被转发到集中位置进行全局模式分析。已经提出了各种综合模型[8,9,13,14,15,16]来从转发的模式中构建全局模式。期望从这样的转发模式合成的规则必须与单一挖掘结果紧密匹配，即，如果将所有数据库放在一起并完成挖掘，则将获得的结果。当模式存在于站点中但不能满足最小支持阈值时，则不允许其参与模式合成过程。因此，此过程可能会丢失一些有趣的模式，这些模式可以帮助决策者做出正确的决策。为了解决这个问题，我们建议在本地和全球采矿过程中整合用户知识。为此，我们通过多层次的规则模式来描述用户的信念和期望，并将它们集成到本地关联规则挖掘和综合过程中。在这种情况下，我们可以获得选定项目的真实全局模式，因为无需估计它们。此外，定义了一种新颖的压缩模式树（CP-TREE）结构，以便存储所有组织级别的候选模式，这可以改善时间处理并减少空间需求。此外，CP-TREE结构有助于探索和投影不同级别的候选模式。最后，我们在生产领域的真实世界数据库中进行了一些实验，并证明了CP-TREE结构对时间处理和空间需求的有效性。

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