The problem of bi-clustering functional data, which has recently been addressed in literature, is considered. A definition of ideal functional bi-cluster is given and a novel bi-clustering method, called Functional Cheng and Church (FunCC), is developed. The introduced algorithm searches for non-overlapping and non-exhaustive bi-clusters in a set of functions which are naturally ordered in matrix structure through a non-parametric deterministic iterative procedure. Moreover, the possible misalignment of the data, which is a common problem when dealing with functions, is taken into account. Hence, the FunCC algorithm is extended obtaining a model able to jointly bi-cluster and align curves. Different simulation studies are performed to show the potential of the introduced method and to compare it with state-of-the-art methods. The model is also applied on a real case study allowing to discover the spatio-temporal patterns of a bike-sharing system.

考虑了最近在文献中已经解决的双集群功能数据的问题。给出了理想的功能性双群集的定义，并开发了一种新的称为功能性程和教堂（FunCC）的双群集方法。引入的算法在一组函数中搜索不重叠且不穷尽的双簇，这些簇通过非参数确定性迭代过程以矩阵结构自然排序。此外，考虑到数据的可能未对准，这是在处理功能时的常见问题。因此，对FunCC算法进行了扩展，获得了能够共同进行双聚类和对齐曲线的模型。进行了不同的模拟研究，以显示引入的方法的潜力，并将其与最新方法进行比较。