面向数据特征的数据划分与加权数据挖掘

Jinmao Wei, Wei-Guo Yi, Ming-Yang Wang, Shuqin Wang
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引用次数: 0

摘要

可以理解的是,无论在数据挖掘中使用哪种模式(并行或顺序),挖掘数据的初始和主要目标都是尽可能多地找到有趣的知识,尽管并行性在实践中也很重要。我们提出了一个原则,称为DFDP,用于划分大数据-并行化的第一步。分区后的数据子集被倾向地处理,以便进行可能的并行或分布式处理。提出了一种可行的并行处理逻辑结构。实验结果表明,加权数据挖掘可以从数据中发现更多有趣的规则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data feature oriented data partition and weighted data mining
It is comprehensible that to find as much interesting knowledge as possible is the initial and main aim to mine data, no matter which pattern (parallel or sequential) is utilized in data mining, though parallelism is practically important as well. We present a principle, called DFDP, for partitioning large dataset-the first step for parallelization. Data subsets after partitioning are treated tendentiously for possible parallel or distributed processing. One feasible logical structure for parallel processing is recommended in the paper. Also experimental comparisons are reported in the paper, which shows that weighted data mining will find more interesting rules from data.
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