基于网格与分形维数的聚类算法

提出了一种基于网格和分形维数的聚类算法，它结合了网格聚类和分形聚类的优点，克服了传统网格聚类算法聚类质量降低的缺点，改进了分形聚类耗时较大的问题。此算法首先根据网格密度得到初始类别，再利用分形的思想，将未被划分的网格依次归类。实验结果证明，它能够发现任意形状且距离非邻近的聚类，且适用于海量、高维数据。

Clustering algorithm based on grid and fractal dimension

Combining the approaches based on grid and fractal, a new kind of clustering algorithm called grid and fractal dimension based clustering algorithm (GFDC) was presented. It overcame the shortcoming of low clustering quality in traditional grid-based clustering method and solved the time-consuming problem of fractal clustering method. In the initial stage of the new algorithm, some initial clusters were got according to the grid density. Then in the expansion stage, the unclassified grids were categorized using the idea of fractal. Experimental results confirm that GFDC is able to capture arbitrary shapes and non-neighboring clustering, and can be applied to the massive and high-dimension dataset.