计算机科学 ›› 2016, Vol. 43 ›› Issue (1): 89-93.doi: 10.11896/j.issn.1002-137X.2016.01.021
王建新,钱宇华
WANG Jian-xin and QIAN Yu-hua
摘要： 近年来符号数据的无监督学习在模式识别、机器学习、数据挖掘和知识发现等诸多领域扮演着越来越重要的角色。然而现有的针对符号数据的聚类算法(经典的K-modes系列算法等),相比数值型数据的聚类算法,在性能方面仍然有很大的提升空间。其根本原因在于符号数据缺乏类似数值数据那样清晰的空间结构。为了能够有效地发掘符号数据内在的空间结构,采用了一种全新的数据表示方案:空间变换方法。该方法将符号数据映射到相应的由原来的属性组成的新的 维度的欧氏空间中。在这一框架的基础上,为了找到符号数据更有代表性的模式,结合Carreira-Perpin提出的K-modes算法进行无监督学习。在9个常用的UCI符号数据集上进行了测试,与传统的符号数据聚类算法进行了实验比较,结果表明几乎在所有的数据集上提出的方法都是更加有效的。
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