基于密度的方法可以发现任意形状的类，但其运算时间复杂度比较大并且不适合于发现分布情况不同的类。
Based density method can find any kind of class but the running time is so long and it can't find the class which has different data distribute.
这种方法使用了基于密度的孤立点挖掘的主要思想，用克隆选择算法进行数据立方体搜索。
It is a dense-based method and the low-dense data cubes are searched by clonal selection algorithm.
在分析常用聚类算法的特点和适应性基础上提出一种基于密度与划分方法的聚类算法。
A clustering algorithm based on density and partitioning method is presented according to the analysis of the strengths and weaknesses of traditional clustering algorithms.
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