绝大多数的聚类分析算法仅能得到单一的聚类结果，考虑到数据的复杂程度普遍较高，以及看待数据的视角不同，所得到的聚类结果在保证其合理性的基础上应当是不唯一的，针对此问题，提出了一个新的算法RLPP，用于发掘多种可供选择的聚类结果。 RLPP 的目标函数兼顾了聚类质量和相异性两大要素，采用子空间流形学习技术，通过新的子空间不断生成多种互不相同的聚类结果。 RLPP 同时适用于线性以及非线性的数据集。实验表明， RLPP成功地发掘了多种可供选择的聚类结果，其性能相当或优于现有的算法。%Most clustering algorithms typically find just one single result for the data inputted. Considering that the complexity of the data is generally high, combined with the need to allow the data to be viewed from different per⁃spectives ( on the basis of ensuring reasonableness) , means that clustering results are often not unique. We present a new algorithm RLPP for an alternative clustering generation method. The objective of RLPP is to find a balance between clustering quality and dissimilarity using a subspace manifold learning technique in a new subspace so that a variety of clustering results can be generated. Experimental results using both linear and nonlinear datasets show that RLPP successfully provides a variety of alternative clustering results, and is able to outperform or at least match a range of existing methods.
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