高维数据聚类 High Dimensional Data Clustering
The running time of DFBC algorithm increases linearity with the increases of combinations. It’s actually a compromise of low-dimensional and high-dimensional clustering method on its idea.
该算法按照维组层次的增长,计算时间也是呈线性变化的,但是就算法的思想来说,它是低维聚类与高维聚类技术的一种折衷。
参考来源 - 高维数据聚类技术中的若干算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
该基于超图的高维聚类算法具有以下特点：1）能处理大数据集；
The algorithm could solve the problems of 1)large volume of data set; 2)data set of high dimension;
针对高维聚类算法——相交网格划分算法GCOD存在的缺陷，提出了基于密度度量的相交网格划分聚类算法IGCOD。
To overcome the shortcomings of the GCOD, a high-dimensional clustering algorithm for data mining, the paper proposes an intersected grid clustering algorithm based on density estimation (IGCOD).
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.
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