基于集成相异矩阵的分类数据聚类
Clustering categorical data via ensembling dissimilarity matrices
摘要：
我们提出了一种聚类分类数据的技术，通过生成许多相异度矩阵，并在他们的平均。我们首先展示了我们的技术在低维分类数据，并将其与其他几种已提出的技术进行比较。然后，我们给出的条件下，我们的方法应该产生良好的结果一般。我们的方法扩展到高维分类数据的长度相等的解释变量的许多选择集成。在这种情况下，我们比较我们的方法与其他两种方法。最后，我们将我们的方法扩展到高维分类数据向量的长度不等，通过使用对齐技术来均衡的长度。我们给出的例子表明，我们的方法继续提供良好的结果，特别是，更好地在基因组序列的背景下比聚类系统发育树建议。
英文摘要：
We present a technique for clustering categorical data by generating many dissimilarity matrices and averaging over them. We begin by demonstrating our technique on low dimensional categorical data and comparing it to several other techniques that have been proposed. Then we give conditions under which our method should yield good results in general. Our method extends to high dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables. In this context we compare our method with two other methods. Finally, we extend our method to high dimensional categorical data vectors of unequal length by using alignment techniques to equalize the lengths. We give examples to show that our method continues to provide good results, in particular, better in the context of genome sequences than clusterings suggested by phylogenetic trees.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
