非均匀类簇密度聚类的多粒度自学习算法

针对非均匀类簇密度聚类问题，从商空间粒度理论出发，提出一种多粒度自学习聚类算法 (multi-granularity self learning clustering algorithm， MSCA)。算法通过构造聚合树结构和定义粒度函数对问题逐层求解，并在每层聚合过程中根据聚合区间以自学习的方式动态确定聚合粒度，解决了传统聚类算法从非均匀类簇密度数据中无法得到不同层次的聚合特征且参数对经验依赖性过高的问题。理论和实验表明，MSCA算法可以发现任意形状类簇，有效处理噪声，并能发现关键聚合层，具有较好的计算复杂性。

Multi-granularity self-learning clustering algorithm for non-uniform cluster density

Based on the quotient space granularity theory，a multi-granularity self learning clustering algorithm (MSCA) is presented for problems with non-uniform cluster density. By constructing a feature clustering tree and defining a granularity function，MSCA solves problems layer by layer and learns clustering granularity dynamically by itself in each step. Traditional clustering algorithms with global parameters cannot discover data features in various layers，and their parameters depend on professional experience seriously，while MSCA can overcome  these defects. Both theory analysis and experimental results show that MSCA can discover key clustering layers and clusters with arbitrary shape. Furthermore，it is insensitive to noise and has a satisfactory computing complexity.