So, distributed and parallel data mining pattern is one of hot problems of research currently.
因此，分布式并行数据挖掘处理模式是目前研究的热点问题之一。
A parallel data mining architecture is put forward, facing to business intelligence and having higher data mining efficiency.
提出了一个面向商业智能的、具有较高数据挖掘效率的并行数据挖掘体系结构。
There were problems in traditional parallel algorithms for mining frequent itemsets more or less: data deviation, large scale communication, frequent synchronization and scanning database.
传统的挖掘频繁项集的并行算法存在数据偏移、通信量大、同步次数较多和扫描数据库次数较多等问题。
Data mining techniques have their origins in methods from statistics, pattern recognition, databases, artificial intelligence, high performance and parallel computing and visualization.
数据挖掘技术起源于从统计方法，模式识别，数据库，人工智能，高性能和并行计算和可视化。
A DOM Tree Alignment Model for Mining Parallel Data from the web.
一种从网上采集相似数据的文档对象模型的树形配置样例。
The emphasis of research is that how to describe and evaluate the refinement result of customer by two data mining methods - concept description and concept parallel.
然后，研究了如何用概念描述和概念对比的数据挖掘方法描述和评估客户细分，这一工作是对在数据挖掘模块中使用聚类算法进行客户细分的完善和补充。
DOM Tree Alignment Model for Mining Parallel Data from the web.
一种从网上采集相似数据的文档对象模型的树形配置样例?。
DOM Tree Alignment Model for Mining Parallel Data from the web.
一种从网上采集相似数据的文档对象模型的树形配置样例?。
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