该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional data mining.
面对大规模、高维的数据，如何建立有效的，可扩展的分类数据挖掘算法是数据挖掘研究的重要方向之一。
Facing the massive volume and high dimensional data, how to build effective and scalable algorithm for data mining is one of research directions of data mining.
面对大规模的、高维的数据，如何建立有效、可扩展的的聚类数据挖掘算法是数据挖掘领域的一个研究热点。
Facing the massive volume and high dimensional data how to build effective and scalable clustering algorithm for data mining is one of research directions of data mining.
针对流程工业数据的高维数、不确定的特点，研究适合处理流程数据的模糊集、粗糙集的粒度数据挖掘理论和方法。
According to the high dimensions and uncertainty of process industrial data, the fuzzy set and rough set of granularity data mining are studied for process data.
因此，对高维数据进行数据挖掘时，必须先对原始数据进行降维处理。
As a result, when doing data mining on high dimensional data, it is necessary to reduce the dimension of primal data at first.
摘要：高维数据之间的相似性度量问题是高维空间数据挖掘中所面临的问题之一。
Absrtact: the problem of similarity measurement between high dimensional data is one of the problems high-dimensional data mining faces.
近年来，随着数据库技术以及数字化技术的不断进步，针对高维时间序列的数据挖掘研究引起了越来越多学者广泛的兴趣。
As the digitalization technology and database technology advanced recent years, data mining techniques that focus on multi-dimensional time series attracts more and more researchers.
该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional dat…
该方法将在高属性维稀疏数据挖掘中起重要的作用。
The algorithm will have important application in high attribute dimensional dat…
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